Triple
T21329056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Hickman |
E525847
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hickman |
—
|
NE NERFINISHED |
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: Hickman | Statement: [Bill Hickman, familyName, Hickman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hickman Context triple: [Bill Hickman, familyName, Hickman]
-
A.
Hickman
chosen
Hickman is an English-origin surname borne by various notable individuals across fields such as politics, sports, and the arts.
-
B.
Harrick
Harrick is a surname most notably associated with Jim Harrick, an American college basketball coach who led UCLA to the 1995 NCAA championship.
-
C.
Erdman
Erdman is a masculine given name most notably borne by Disney story artist and screenwriter Erdman Penner.
-
D.
Hooperman
Hooperman is an American television dramedy series from the late 1980s starring John Ritter as a San Francisco police inspector balancing his personal and professional life.
-
E.
Hickes
Hickes is a work or concept derived from or developed upon the earlier basis provided by Hicke.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51b90788190a4dd823d962626da |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7ab4f95fc819087eb32dca7da689a |
completed | April 21, 2026, 4:52 p.m. |
Created at: April 16, 2026, 4:42 p.m.