Triple
T20280846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chauncey, Ohio |
E503136
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Ohio |
—
|
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: Ohio | Statement: [Chauncey, Ohio, partOf, Ohio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ohio Context triple: [Chauncey, Ohio, partOf, Ohio]
-
A.
Ohio
chosen
Ohio is a Midwestern U.S. state known for its diverse economy, major cities like Columbus, Cleveland, and Cincinnati, and its significant role in national politics as a historic swing state.
-
B.
Ohio
"Ohio" is a classic protest song by Crosby, Stills, Nash & Young, written by Neil Young in response to the 1970 Kent State shootings.
-
C.
Ohio, Illinois
Ohio, Illinois is a small rural village in Bureau County known for its agricultural surroundings and quiet small-town character.
-
D.
Indiana
"Indiana" is a jazz standard widely performed and recorded by saxophonist Don Byas, showcasing his virtuosic improvisational style.
-
E.
Indiana
Indiana is a U.S. state known for its manufacturing base, rich agricultural land, and iconic events like the Indianapolis 500.
- 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_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6768e9f0881909c8fe8772dafd468 |
completed | April 20, 2026, 6:55 p.m. |
Created at: April 16, 2026, 10:38 a.m.