Triple

T2175299
Position Surface form Disambiguated ID Type / Status
Subject DocBook E48511 entity
Predicate relatedTo P37 FINISHED
Object TEI
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.
 E242855 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: TEI | Statement: [DocBook, relatedTo, TEI]
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.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: TEI
Triple: [DocBook, relatedTo, TEI]
Generated 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.
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

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a88aa3faa48190995b233af6525815 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abbece30888190936853740ff6cb02 completed March 7, 2026, 5:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5d9eff988190a02734bd73616cba completed March 9, 2026, 5:41 a.m.
NEDg Description generation batch_69ae5e5f023081909cd046b5850f8026 completed March 9, 2026, 5:45 a.m.
NED2 Entity disambiguation (via description) batch_69ae5ef99018819083a778378ea493e8 completed March 9, 2026, 5:47 a.m.
Created at: March 4, 2026, 7:45 p.m.