Triple
T11809060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McCone Hall |
E280822
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | McCone |
E623681
|
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: McCone | Statement: [McCone Hall, shortName, McCone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: McCone Context triple: [McCone Hall, shortName, McCone]
-
A.
McCone
chosen
McCone is a surname most notably associated with John A. McCone, an American industrialist and former Director of the Central Intelligence Agency.
-
B.
McCauley
McCauley is the maiden surname of Rosa Parks, the prominent American civil rights activist known for her pivotal role in the Montgomery bus boycott.
-
C.
McKimson
McKimson is a surname most notably associated with American animator and director Robert McKimson, known for his work on classic Warner Bros. cartoons.
-
D.
Mackenzell
Mackenzell is a small village in the Hesse region of central Germany.
-
E.
Howden
Howden is a historic market town in the East Riding of Yorkshire, England, known for its medieval Minster and traditional town centre.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a5c93a1881909b428bf3ed55bb57 |
completed | April 10, 2026, 7:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f13188b89c819095ba5d27de7ebbb2 |
completed | April 28, 2026, 10:15 p.m. |
Created at: April 8, 2026, 9:42 p.m.