Triple
T3067597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hana Airport |
E62141
|
entity |
| Predicate | distanceFromHanaTown (miles) |
P44219
|
FINISHED |
| Object | about 3 |
—
|
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: about 3 | Statement: [Hana Airport, distanceFromHanaTown (miles), about 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromHanaTown (miles) Context triple: [Hana Airport, distanceFromHanaTown (miles), about 3]
-
A.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
B.
approximateLengthInMiles
Indicates the estimated distance or extent of something measured in miles.
-
C.
distanceToMilwaukee
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Milwaukee.
-
D.
distanceFromMainland
Indicates the measured spatial separation between a location and the nearest point on the mainland.
-
E.
distanceFromLosAngeles
Indicates the measured or specified distance between a given entity’s location and the city of Los Angeles.
- 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_69ad85793e5c8190a358049bc4a98d8c |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada0fea06881909e5251eea26599ac |
completed | March 8, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69ad9624b7a0819091d255614f5819ea |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f7630c81908e1ca8a69611cff6 |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 3:02 p.m.