Triple
T27178504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chauvigny |
E683119
|
entity |
| Predicate | distanceToPoitiersKilometers |
P123619
|
FINISHED |
| Object | approximately 23 |
—
|
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: approximately 23 | Statement: [Chauvigny, distanceToPoitiersKilometers, approximately 23]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToPoitiersKilometers Context triple: [Chauvigny, distanceToPoitiersKilometers, approximately 23]
-
A.
distanceToPoitiers
chosen
Indicates the spatial distance between a given entity and the location of Poitiers.
-
B.
distanceFromAngersKilometres
Indicates the physical distance, measured in kilometers, between an entity and the location of Angers.
-
C.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
D.
distanceToPauKmApprox
Indicates an approximate distance, measured in kilometers, from an entity to the location Pau.
-
E.
distanceToSaint-Étienne
Indicates the measured or specified distance between a given entity and the location Saint-Étienne.
- 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_69eefad086808190ab89816c0c300476 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69ff17be6ad48190963206f2619b1b28 |
completed | May 9, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69ff1724ba24819092c928fcbcb286ec |
completed | May 9, 2026, 11:14 a.m. |
Created at: April 27, 2026, 9:27 a.m.