Triple
T7394546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albert J. Beveridge Award |
E170588
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object | Daniel Walker Howe |
E126486
|
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: Daniel Walker Howe | Statement: [Albert J. Beveridge Award, notableRecipient, Daniel Walker Howe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Walker Howe Context triple: [Albert J. Beveridge Award, notableRecipient, Daniel Walker Howe]
-
A.
David T. Wilentz
David T. Wilentz was a prominent American lawyer and politician best known as the New Jersey Attorney General who prosecuted the Lindbergh kidnapping case.
-
B.
Gordon S. Wood
chosen
Gordon S. Wood is a prominent American historian renowned for his influential scholarship on the American Revolution and the early United States.
-
C.
J. O. Taylor
J. O. Taylor was a cinematographer active during early Hollywood who worked on the landmark 1933 monster film "King Kong."
-
D.
Eric Foner
Eric Foner is a prominent American historian best known for his influential scholarship on the Civil War, Reconstruction, and American freedom.
-
E.
Alan Taylor
Alan Taylor is an American film and television director known for his work on major projects such as "Game of Thrones," "Thor: The Dark World," and "Terminator Genisys."
- 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_69c68a5f04188190ac266569c9280347 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2263b48819089319a2a2f0d3357 |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c82772400881908d6b11b60a1443bb |
completed | March 28, 2026, 7:09 p.m. |
Created at: March 27, 2026, 3:09 p.m.