Triple
T34102876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beautiran |
E874619
|
entity |
| Predicate | distanceFromBordeauxKilometers |
P90200
|
FINISHED |
| Object | about 20 |
—
|
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 | Statement: [Beautiran, distanceFromBordeauxKilometers, about 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBordeauxKilometers Context triple: [Beautiran, distanceFromBordeauxKilometers, about 20]
-
A.
distanceToBordeauxCenter
chosen
Indicates the measured or calculated distance between a given entity’s location and the center of Bordeaux.
-
B.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
C.
distanceFromNiceByRoad_km
Indicates the length of the road route, in kilometers, from the city of Nice to the given location.
-
D.
distanceFromParisCenter
Indicates the measured distance between a given location and the central point of Paris.
-
E.
distanceToDijon_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Dijon.
- 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_69f349a80d4481908527317d43f5c579 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f963908190846d232f386fd98f |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:53 a.m.