Triple
T22012170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KHVN |
E543603
|
entity |
| Predicate | isInFAAType |
P146271
|
FINISHED |
| Object | primary commercial service airport |
—
|
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: primary commercial service airport | Statement: [KHVN, isInFAAType, primary commercial service airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInFAAType Context triple: [KHVN, isInFAAType, primary commercial service airport]
-
A.
hasFaithfulType
Indicates that one entity has a corresponding type or classification that remains consistent, reliable, or invariant with respect to some underlying structure or mapping.
-
B.
hasGoodType
Indicates that an entity possesses a type or classification considered appropriate, valid, or of high quality according to some defined criteria.
-
C.
isLargestTypeOf
Indicates that one entity represents the largest variant, category, or instance within a specified type or group relative to others.
-
D.
hasFrameType
Indicates that an entity possesses or is associated with a specific type or category of frame.
-
E.
isNotType
Indicates that one entity is explicitly not of a specified type or category in relation to another.
- 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_69e11e2db934819095556760c7d85e4d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127a520bc8190865f525a87255fb2 |
completed | April 28, 2026, 9:33 p.m. |
| PD | Predicate disambiguation | batch_69e6f62dc9d88190ae387f145f9528de |
completed | April 21, 2026, 3:59 a.m. |
| PDg | Predicate description generation | batch_69e6fad4a540819096cdd5ea08527220 |
completed | April 21, 2026, 4:19 a.m. |
Created at: April 16, 2026, 8:22 p.m.