Triple
T23632934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vagar Airport |
E583663
|
entity |
| Predicate | distanceToTórshavnKm |
P69089
|
FINISHED |
| Object | about 47 |
—
|
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 47 | Statement: [Vagar Airport, distanceToTórshavnKm, about 47]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToTórshavnKm Context triple: [Vagar Airport, distanceToTórshavnKm, about 47]
-
A.
distance to Tórshavn (kilometers)
chosen
Indicates the length, in kilometers, of the shortest travel distance between an entity and the location Tórshavn.
-
B.
distanceToLerwickKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the town of Lerwick.
-
C.
distanceFromReykjavík
Indicates the spatial distance between an entity’s location and the city of Reykjavík.
-
D.
distanceFromLongyearbyen
Indicates the measured distance between a given location and Longyearbyen.
-
E.
distanceFromCopenhagen
Indicates the spatial distance between a given entity and the location of Copenhagen.
- F. None of above.
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_69e248fe1c2c8190ac914d2442ff3d26 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b1ea269c8190a677e812d7471a98 |
completed | April 29, 2026, 7:23 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:47 p.m.