Triple
T23227717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pamiers |
E581057
|
entity |
| Predicate | distanceToToulouse |
P151441
|
FINISHED |
| Object | approximately 60 km south |
—
|
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 60 km south | Statement: [Pamiers, distanceToToulouse, approximately 60 km south]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToToulouse Context triple: [Pamiers, distanceToToulouse, approximately 60 km south]
-
A.
distanceFromToulouse
Indicates the measured spatial distance between a given entity and the location of Toulouse.
-
B.
distanceToToulon_km
Indicates the distance, measured in kilometers, between a given entity’s location and the city of Toulon.
-
C.
distanceToMontpellierKm
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Montpellier.
-
D.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
E.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
- 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_69e246043c48819089bae72c9a9c306c |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f1922f5b4081908145d66ea7534493 |
completed | April 29, 2026, 5:07 a.m. |
| PD | Predicate disambiguation | batch_69effcdadec0819092ec1749ee453b4e |
completed | April 28, 2026, 12:18 a.m. |
| PDg | Predicate description generation | batch_69f01d8770d081908897c28b04e5faea |
completed | April 28, 2026, 2:37 a.m. |
Created at: April 17, 2026, 4:09 p.m.