Triple
T19849527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gloria Jean Watkins |
E476957
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Watkins |
—
|
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: Watkins | Statement: [Gloria Jean Watkins, familyName, Watkins]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Watkins Context triple: [Gloria Jean Watkins, familyName, Watkins]
-
A.
Watkins
chosen
Watkins is a surname most prominently associated with Sherron Watkins, the former Enron vice president who became widely known as a corporate whistleblower.
-
B.
Bevin
Bevin is a surname most notably associated with Ernest Bevin, a prominent British Labour politician and post–World War II Foreign Secretary.
-
C.
Qualley
Qualley is the surname of an American family best known for actress and model Margaret Qualley and her mother, actress Andie MacDowell.
-
D.
Hayes
Hayes is a common English surname borne by numerous notable figures in politics, entertainment, sports, and other fields.
-
E.
Hayes
Hayes is a suburban town in west London, England, known for its residential areas, transport links, and proximity to Heathrow Airport.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658664368819082783bc40342a26a |
completed | April 20, 2026, 4:46 p.m. |
Created at: April 10, 2026, 1:51 p.m.