TEI
E242855
TEI (Text Encoding Initiative) is a widely used standard for encoding and representing texts in digital form, especially in the humanities, using XML-based guidelines.
All labels observed (4)
| Label | Occurrences |
|---|---|
| TEI canonical | 2 |
| TEI Guidelines | 1 |
| TEI P5 | 1 |
| TEI header | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2175299 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TEI Context triple: [DocBook, relatedTo, TEI]
-
A.
METS
METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
-
B.
TXL
TXL was the IATA airport code for Berlin Tegel Airport, the former main international airport of Berlin, Germany.
-
C.
Corpus
Corpus is a common shortened name for Corpus Christi College, one of the historic constituent colleges of the University of Cambridge.
-
D.
Tegsedi
Tegsedi is an antisense oligonucleotide drug used to treat hereditary transthyretin-mediated amyloidosis by reducing the production of the transthyretin protein.
-
E.
MARC
MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TEI Target entity description: TEI (Text Encoding Initiative) is a widely used standard for encoding and representing texts in digital form, especially in the humanities, using XML-based guidelines.
-
A.
METS
METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
-
B.
TXL
TXL was the IATA airport code for Berlin Tegel Airport, the former main international airport of Berlin, Germany.
-
C.
Corpus
Corpus is a common shortened name for Corpus Christi College, one of the historic constituent colleges of the University of Cambridge.
-
D.
Tegsedi
Tegsedi is an antisense oligonucleotide drug used to treat hereditary transthyretin-mediated amyloidosis by reducing the production of the transthyretin protein.
-
E.
MARC
MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
XML-based markup language
ⓘ
digital humanities standard ⓘ text encoding standard ⓘ |
| abbreviation | TEI self-link ⓘ |
| basedOn | SGML ⓘ |
| compatibleWith | Unicode ⓘ |
| currentMainVersion | P5 ⓘ |
| developedBy |
Text Encoding Initiative
ⓘ
surface form:
Text Encoding Initiative Consortium
|
| domain |
digital humanities
ⓘ
scholarly editing ⓘ text encoding ⓘ |
| fullName | Text Encoding Initiative ⓘ |
| governedBy |
Text Encoding Initiative
ⓘ
surface form:
Text Encoding Initiative Consortium
|
| hasComponent |
TEI
self-linksurface differs
ⓘ
surface form:
TEI Guidelines
TEI self-linksurface differs ⓘ
surface form:
TEI P5
TEI attributes ⓘ TEI elements ⓘ TEI self-linksurface differs ⓘ
surface form:
TEI header
TEI schema ⓘ |
| introduced | late 1980s ⓘ |
| previousVersion |
P3
ⓘ
P4 ⓘ |
| primaryFormat | XML ⓘ |
| provides |
ODD customization language
ⓘ
customization mechanisms ⓘ documentation for text encoding ⓘ |
| purpose |
encoding texts in digital form
ⓘ
representing complex textual features ⓘ standardizing text markup practices ⓘ supporting scholarly analysis of texts ⓘ |
| supports |
corpora encoding
ⓘ
critical editions ⓘ drama encoding ⓘ epigraphic texts ⓘ historical documents ⓘ linguistic annotation ⓘ literary texts ⓘ manuscript description ⓘ metadata description ⓘ names and dates encoding ⓘ poetry encoding ⓘ semantic markup ⓘ structural markup ⓘ |
| usedFor |
interchange of encoded texts
ⓘ
long-term preservation of texts ⓘ machine-readable text representation ⓘ |
| widelyUsedIn |
archives
ⓘ
digital libraries ⓘ digital scholarly editions ⓘ libraries ⓘ research projects ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: TEI Description of subject: TEI (Text Encoding Initiative) is a widely used standard for encoding and representing texts in digital form, especially in the humanities, using XML-based guidelines.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
TEI Guidelines
this entity surface form:
TEI P5
this entity surface form:
TEI header