Triple
T9452032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Ken |
E227915
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Allison Park |
E396357
|
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: Allison Park | Statement: [Dr. Ken, hasMainCharacter, Allison Park]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allison Park Context triple: [Dr. Ken, hasMainCharacter, Allison Park]
-
A.
Allison Park, Pennsylvania
chosen
Allison Park, Pennsylvania is a suburban community in Allegheny County, north of Pittsburgh, known as a residential area within the Pittsburgh metropolitan region.
-
B.
Bethel Park
Bethel Park is a suburban municipality in southwestern Pennsylvania, located just south of Pittsburgh.
-
C.
Wilkinsburg
Wilkinsburg is a borough in Allegheny County, Pennsylvania, known as an inner-ring suburb directly east of Pittsburgh.
-
D.
Garfield Heights
Garfield Heights is a suburban city located southeast of downtown Cleveland in northeastern Ohio.
-
E.
Upper St. Clair
Upper St. Clair is a suburban township in southwestern Pennsylvania known for its affluent residential character and highly ranked public school system.
- 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_69ca8439f8bc8190997f2ef40c9f0bc2 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7f6796d8819088cbecc0588ae5fc |
completed | April 1, 2026, 8:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12272a2dc8190892a22db0799b6a4 |
completed | April 4, 2026, 2:38 p.m. |
Created at: March 30, 2026, 7:52 p.m.