Triple
T23525634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Sebastián Airport |
E576423
|
entity |
| Predicate | distanceToSanSebastián |
P153102
|
FINISHED |
| Object | about 20 km |
—
|
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 20 km | Statement: [San Sebastián Airport, distanceToSanSebastián, about 20 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSanSebastián Context triple: [San Sebastián Airport, distanceToSanSebastián, about 20 km]
-
A.
distanceToBahíaBlanca
Indicates the measured distance between a given entity and the location of Bahía Blanca.
-
B.
distanceToPuntaDelEste
Indicates the measured distance between a given entity’s location and the location of Punta del Este.
-
C.
distanceFromSanJuan
Indicates the measured distance separating an entity from the location of San Juan.
-
D.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
E.
distanceToNeuquénCity_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Neuquén.
- 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_69e245f5a8848190a2ba42e271c6c31f |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ac73be64819083e4a1c2c09551fb |
completed | April 29, 2026, 7 a.m. |
| PD | Predicate disambiguation | batch_69f1189d75b48190a1c01928a993c9fb |
completed | April 28, 2026, 8:29 p.m. |
| PDg | Predicate description generation | batch_69f12760784c8190aaeff002ef31febe |
completed | April 28, 2026, 9:32 p.m. |
Created at: April 17, 2026, 6:09 p.m.