Triple
T12562799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hillegas |
E295390
|
entity |
| Predicate | notableBearer |
P458
|
FINISHED |
| Object | Michael Hillegas |
E61033
|
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: Michael Hillegas | Statement: [Hillegas, notableBearer, Michael Hillegas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Hillegas Context triple: [Hillegas, notableBearer, Michael Hillegas]
-
A.
Michael Hillegas
chosen
Michael Hillegas was an American merchant and statesman who served as the first Treasurer of the United States during the Revolutionary era.
-
B.
Alex Heineman
Alex Heineman is a film producer known for his work on the historical thriller "Operation Finale" and other feature films.
-
C.
Matthew C. Brown
Matthew C. Brown is a film producer known for his work on the horror movie "Spectral."
-
D.
Andy Ernst
Andy Ernst is a music producer best known for his work on early punk rock and alternative albums, including Green Day’s breakthrough record "Kerplunk."
-
E.
Michael Grunst
Michael Grunst is a German local politician who serves as the borough mayor of Berlin’s Lichtenberg district.
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95494ae1c81908b9ee14b8ef92a65 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6558da7e0819086860bfaf394e2d8 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 11:48 p.m.