Triple
T18308709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doug Belgrad |
E438557
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Hancock |
—
|
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: Hancock | Statement: [Doug Belgrad, notableWork, Hancock]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hancock Context triple: [Doug Belgrad, notableWork, Hancock]
-
A.
Hancock
Hancock is a prominent surname most famously associated with John Hancock, a key figure of the American Revolution and first signer of the United States Declaration of Independence.
-
B.
Hancock
Hancock is a small rural town in western Massachusetts known for its scenic Berkshire landscapes and outdoor recreation.
-
C.
Hancock
Hancock is a small city in Michigan’s Upper Peninsula known for its Finnish-American heritage and proximity to Lake Superior.
-
D.
Hancock (film)
chosen
Hancock is a 2008 superhero action-comedy film starring Will Smith as a troubled, alcoholic superhero seeking redemption in modern-day Los Angeles.
-
E.
Kingman
Kingman is a champion British Thoroughbred racehorse renowned for his exceptional speed and success in top-level mile races.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021709f88190a8047dd57edc2029 |
completed | April 19, 2026, 4:25 p.m. |
Created at: April 10, 2026, 10:35 a.m.