Triple
T18814043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ancient Egypt gallery |
E460087
|
entity |
| Predicate | languageOfExplanatoryText |
P129014
|
FINISHED |
| Object | local language of the museum |
—
|
LITERAL FINISHED |
How this triple was built (2 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: local language of the museum | Statement: [Ancient Egypt gallery, languageOfExplanatoryText, local language of the museum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfExplanatoryText Context triple: [Ancient Egypt gallery, languageOfExplanatoryText, local language of the museum]
-
A.
languageText
chosen
Indicates that a piece of text is expressed in, or associated with, a particular language.
-
B.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
C.
languageSpecifies
Indicates that one entity defines or constrains the syntax, semantics, or usage rules that govern how another language or linguistic system is expressed or interpreted.
-
D.
languageOfWritings
Indicates that a specified language is the one in which certain writings or written works are composed.
-
E.
languageOfTextInImages
Indicates the language used in the textual content that appears within images.
- F. None of above.
Provenance (3 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a3df2d3881909b336d813bbfd0aa |
completed | April 20, 2026, 3:56 a.m. |
| PD | Predicate disambiguation | batch_69e48d1b10ec8190985c6fb5766ff981 |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:53 a.m.