Triple
T16860558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gilgit Airport |
E409897
|
entity |
| Predicate | hasSmallTerminalBuilding |
P125265
|
FINISHED |
| Object | true |
—
|
LITERAL 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: true | Statement: [Gilgit Airport, hasSmallTerminalBuilding, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSmallTerminalBuilding Context triple: [Gilgit Airport, hasSmallTerminalBuilding, true]
-
A.
hasTerminalBuildings
Indicates that one entity possesses or includes terminal buildings associated with it.
-
B.
isSmall
Indicates that one entity has a size that is relatively small, either in absolute terms or compared to a reference standard or another entity.
-
C.
isSmallScale
Indicates that the related activity, operation, or entity occurs on a limited or minor scale, involving relatively small size, scope, or capacity.
-
D.
hasMiniature
Indicates that one entity possesses or includes a smaller-scale representation or model of another entity.
-
E.
isSmallCity
Indicates that a city has a relatively small population size or limited geographic/urban extent compared to typical cities.
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b502bc048190baa5a83015407080 |
completed | April 18, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69e32b8cbb048190878a259cc5be960e |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e355722040819098830dabf207ecd6 |
completed | April 18, 2026, 9:57 a.m. |
Created at: April 10, 2026, 5:24 a.m.