Triple
T25318556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ozark, Alabama |
E634814
|
entity |
| Predicate | previousNameOfNearbyBase |
P167711
|
FINISHED |
| Object | Fort Rucker |
—
|
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: Fort Rucker | Statement: [Ozark, Alabama, previousNameOfNearbyBase, Fort Rucker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousNameOfNearbyBase Context triple: [Ozark, Alabama, previousNameOfNearbyBase, Fort Rucker]
-
A.
hasNearbyBase
Indicates that one entity has a base or facility located in close physical proximity to another entity or location.
-
B.
nearbyTownFormerName
Indicates that the nearby town previously had a different name, specifying its former name in relation to the current nearby town.
-
C.
previousLocationName
Indicates that one entity specifies the name of a location where another entity was situated or occurred before its current location.
-
D.
formerlyKnownLocationAs
Indicates that an entity was previously known or referred to by a different location name or designation.
-
E.
nearbyResearchStationFormerName
Indicates that a nearby research station previously had a different name, specifying that former name.
- F. None of above. chosen
Provenance (4 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_69e75a9847c08190bb02990d06d5ffb7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f66cf092c881908d7034c9c2bc61d5 |
completed | May 2, 2026, 9:30 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69f66c59de9881909ebbb7b0ae7ab495 |
completed | May 2, 2026, 9:27 p.m. |
Created at: April 21, 2026, 1:28 p.m.