Triple
T3339660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michigan Stadium |
E70225
|
entity |
| Predicate | architect |
P184
|
FINISHED |
| Object | Bernard L. Green |
E380064
|
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: Bernard L. Green | Statement: [Michigan Stadium, architect, Bernard L. Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bernard L. Green Context triple: [Michigan Stadium, architect, Bernard L. Green]
-
A.
Bernard L. Green
chosen
Bernard L. Green was an architect known for designing the building known as The Big House.
-
B.
George A. Bermann
George A. Bermann is a prominent American legal scholar and expert in international and comparative law, particularly known for his work in international arbitration.
-
C.
Lionel M. Bender
Lionel M. Bender was an American linguist known for his extensive work on African languages, particularly within the Nilo-Saharan and Afroasiatic families.
-
D.
Gerald B. Greenberg
Gerald B. Greenberg is an American film editor best known for his Academy Award–winning work on the 1979 drama "Kramer vs. Kramer."
-
E.
Cecil H. Green
Cecil H. Green was a British-born American geophysicist, entrepreneur, and philanthropist best known as a co-founder of Texas Instruments and a major benefactor of educational and research institutions.
- 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_69ad85a405e48190b6e68de7cf9f319e |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1bf1f648190993ac8e9dda60983 |
completed | March 8, 2026, 5:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b503d6cd9c81908acb288091503ec1 |
completed | March 14, 2026, 6:44 a.m. |
Created at: March 8, 2026, 3:12 p.m.