Triple
T10218342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom McHale |
E242505
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | McHale |
E49874
|
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: McHale | Statement: [Tom McHale, familyName, McHale]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: McHale Context triple: [Tom McHale, familyName, McHale]
-
A.
McHale
chosen
McHale is an Irish surname borne by various notable figures in sports, entertainment, and public life.
-
B.
Jack McHale
Jack McHale is a relatively obscure individual known primarily as a namesake referenced in records of notable bearers of the surname McHale.
-
C.
Max O’Hara
Max O’Hara is a fast-talking, ambitious showman and nightclub promoter who brings the giant gorilla Joe to Hollywood in the 1949 adventure film "Mighty Joe Young."
-
D.
Hagey
Hagey is a surname most notably associated with Gerald Hagey, a prominent Canadian academic and founding president of the University of Waterloo.
-
E.
Bill Calhoun
Bill Calhoun is a charming, gambling-prone dancer and actor who serves as one of the principal comic romantic leads in the musical "Kiss Me, Kate."
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa6fcf6c81908a589585bbd48ab8 |
completed | April 6, 2026, 12:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87e27c2dc8190a5b3173fbcb01114 |
completed | April 10, 2026, 4:35 a.m. |
Created at: April 6, 2026, 11:07 a.m.