Triple
T820554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alabama |
E17742
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Anniston |
E66579
|
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: Anniston | Statement: [Alabama, containsCity, Anniston]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anniston Context triple: [Alabama, containsCity, Anniston]
-
A.
Anniston, Alabama
chosen
Anniston, Alabama is a small city in northeastern Alabama known historically as a planned industrial community and gateway to the nearby Appalachian foothills.
-
B.
Phenix City
Phenix City is a city in eastern Alabama located along the Chattahoochee River, directly across from Columbus, Georgia.
-
C.
Prattville
Prattville is a city in central Alabama known for its historic downtown, proximity to Montgomery, and strong ties to the manufacturing and paper industries.
-
D.
Montgomery
Montgomery is a historic market town in Powys, Wales, known for its medieval castle ruins and Georgian architecture.
-
E.
Montgomery
Montgomery is a common English and Scottish surname of Norman origin, historically associated with nobility and military figures.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab6698d881908d8c5d91259f97ec |
completed | March 1, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6efcc37c81908fd49ad65818eb0c |
completed | March 7, 2026, 6:31 p.m. |
Created at: March 1, 2026, 7:38 p.m.