Triple
T6484930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bullock County |
E146483
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Union Springs |
E597172
|
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: Union Springs | Statement: [Bullock County, hasCity, Union Springs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Union Springs Context triple: [Bullock County, hasCity, Union Springs]
-
A.
Union Springs
chosen
Union Springs is a small city in southeastern Alabama that serves as the administrative and cultural center of Bullock County.
-
B.
Union Springs, New York
Union Springs, New York is a small village in Cayuga County known for its scenic location on the eastern shore of Cayuga Lake in the Finger Lakes region.
-
C.
Fairlawn
Fairlawn is a residential neighborhood and community within the city of Pawtucket in Providence County, Rhode Island.
-
D.
Lastoursville
Lastoursville is a town in central Gabon known as a regional hub along the Trans-Gabon Railway and for its nearby cave systems and karst landscapes.
-
E.
Elmwood Springs
Elmwood Springs is a small, close-knit fictional American town created by Fannie Flagg and used as the setting for several of her interconnected novels.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a6efe1881909a044b1cdaa511af |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c66385943081908630aa0f2cdefa06 |
completed | March 27, 2026, 11:01 a.m. |
Created at: March 22, 2026, 4:52 p.m.