Triple
T6831329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jonas Salk |
E157143
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Pittsburgh |
E19280
|
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: Pittsburgh | Statement: [Jonas Salk, workLocation, Pittsburgh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pittsburgh Context triple: [Jonas Salk, workLocation, Pittsburgh]
-
A.
Pittsburg
Pittsburg is an industrial and residential city in Contra Costa County in the San Francisco Bay Area of California.
-
B.
Pittsburgh, Pennsylvania
chosen
Pittsburgh, Pennsylvania is a major U.S. city in western Pennsylvania known for its historic steel industry, numerous bridges, and strong educational and technology sectors.
-
C.
Duquesne, Pennsylvania
Duquesne, Pennsylvania is a small industrial city along the Monongahela River near Pittsburgh, historically known as a major steel-producing community in the American Rust Belt.
-
D.
Oakland, Pittsburgh
Oakland is a major Pittsburgh neighborhood known as the city’s academic and medical hub, home to institutions like the University of Pittsburgh and Carnegie Mellon University.
-
E.
Carnegie, Pennsylvania
Carnegie, Pennsylvania is a small borough in Allegheny County near Pittsburgh, historically tied to the region’s steel industry and local immigrant communities.
- 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_69c6882a5b5c8190917a7db9ed36bad1 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d62992908190996efab71cbf70f0 |
completed | March 27, 2026, 7:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72f8255ac81909e7732947d2a5f53 |
completed | March 28, 2026, 1:31 a.m. |
Created at: March 27, 2026, 2:18 p.m.