Triple
T600340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air University |
E11478
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Montgomery, Alabama |
E9524
|
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: Montgomery, Alabama | Statement: [Air University, locatedIn, Montgomery, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montgomery, Alabama Context triple: [Air University, locatedIn, Montgomery, Alabama]
-
A.
Montgomery, Alabama
chosen
Montgomery, Alabama is the state capital known as a pivotal center of the American civil rights movement, including events such as the Montgomery Bus Boycott.
-
B.
Montgomery
Montgomery is a common English and Scottish surname of Norman origin, historically associated with nobility and military figures.
-
C.
Gadsden, Alabama
Gadsden, Alabama is a small industrial city in northeastern Alabama known historically for its manufacturing plants and labor history.
-
D.
Bessemer, Alabama
Bessemer, Alabama is an industrial city in Jefferson County that forms part of the greater Birmingham region in central Alabama.
-
E.
Tuscaloosa, Alabama
Tuscaloosa, Alabama is a city in western Alabama known as the home of the University of Alabama and a regional center for education, healthcare, and industry.
- 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49d7a2180819086c7e9465a2d7432 |
completed | March 1, 2026, 8:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a521704f3081909c1dbb3b030e79ec |
completed | March 2, 2026, 5:34 a.m. |
Created at: March 1, 2026, 7:35 p.m.