Triple
T8956727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winston County |
E213488
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Haleyville |
E767463
|
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: Haleyville | Statement: [Winston County, hasCity, Haleyville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haleyville Context triple: [Winston County, hasCity, Haleyville]
-
A.
Haleyville
chosen
Haleyville is a small city in northwestern Alabama known for being the largest municipality in Winston County and for hosting the first 9-1-1 emergency call in the United States.
-
B.
Lynnville
Lynnville is a small rural town located in central Iowa, United States.
-
C.
Fairhope
Fairhope is a small, picturesque city on the eastern shore of Mobile Bay in southern Alabama, known for its arts community, scenic waterfront, and historic downtown.
-
D.
Haleburg, Alabama
Haleburg, Alabama is a small rural town located in southeastern Alabama near the Georgia state line.
-
E.
Helena, Alabama
Helena, Alabama is a suburban city in central Alabama known for its family-friendly neighborhoods, strong schools, and proximity to Birmingham.
- 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_69ca8399ad2081909f8fa41d4314c215 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6728965881908e9f14aaee0c5a18 |
completed | April 1, 2026, 12:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc93ed3308190a39069d7b7d4ea3d |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 7 p.m.