Triple
T5088652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black on Maroon |
E114698
|
entity |
| Predicate | ownedBy |
P347
|
FINISHED |
| Object | Tate |
E86288
|
NE 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: Tate | Statement: [Black on Maroon, ownedBy, Tate]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tate Context triple: [Black on Maroon, ownedBy, Tate]
-
A.
Tate
chosen
Tate is a UK-based art institution and network of galleries that houses and promotes the national collection of British art and significant international modern and contemporary works.
-
B.
Bridgeman
Bridgeman is an English surname historically associated with British nobility and aristocratic families.
-
C.
Tibbetts
Tibbetts is a surname of English origin borne by various notable individuals across fields such as politics, sports, and the arts.
-
D.
Tucker
Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
-
E.
Artis Mills
Artis Mills is best known as the longtime husband of legendary American singer Etta James.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd443e941881908eb4e8c685b6f656 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd753f6544819090c028b34ee87536 |
completed | March 20, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba72a7b88190a118ff5f31079eff |
completed | March 21, 2026, 3:34 p.m. |
Created at: March 20, 2026, 1:40 p.m.