Triple
T23355484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sidney Bernstein |
E593034
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Manchester |
—
|
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: Manchester | Statement: [Sidney Bernstein, workLocation, Manchester]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manchester Context triple: [Sidney Bernstein, workLocation, Manchester]
-
A.
Manchester
Manchester is a common English surname, notably borne by American singer and songwriter Melissa Manchester.
-
B.
Manchester
chosen
Manchester is a major city in northwest England known for its industrial heritage, vibrant cultural scene, and influential contributions to music, sport, and science.
-
C.
Manchester
Manchester is the most populous city in the U.S. state of New Hampshire and a major economic and cultural center for the region.
-
D.
Manchester
Manchester is a suburban town in central Connecticut known for its historic mills, shopping districts, and residential communities within the Greater Hartford area.
-
E.
Manchester
Manchester is a historic former settlement in what is now Cuyahoga Falls, Ohio, that served as an early name and precursor community to the modern city.
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19a176b548190bf5a08bb2585344d |
completed | April 29, 2026, 5:41 a.m. |
Created at: April 17, 2026, 5:26 p.m.