Triple
T6714958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andre Harrell |
E153243
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Harrell |
E541983
|
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: Harrell | Statement: [Andre Harrell, familyName, Harrell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harrell Context triple: [Andre Harrell, familyName, Harrell]
-
A.
Harrell
chosen
Harrell is a surname of English origin borne by various notable individuals across fields such as politics, sports, and the arts.
-
B.
Darrell
Darrell is the central protagonist of the film "In the Mix," around whom the story’s main events and conflicts revolve.
-
C.
LeRoy
LeRoy is the middle name of American political consultant and Republican strategist Lee Atwater.
-
D.
Helton
Helton is a surname most prominently associated with former Major League Baseball first baseman Todd Helton, a longtime star for the Colorado Rockies.
-
E.
Hagey
Hagey is a surname most notably associated with Gerald Hagey, a prominent Canadian academic and founding president of the University of Waterloo.
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d1246b748190aed94e8ab8625f7e |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70096e05c8190abfa90996db37eeb |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:07 p.m.