Triple
T4235823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pamiers |
E94688
|
entity |
| Predicate | distanceFromFoixKilometres |
P54846
|
FINISHED |
| Object | approximately 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: approximately 20 | Statement: [Pamiers, distanceFromFoixKilometres, approximately 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromFoixKilometres Context triple: [Pamiers, distanceFromFoixKilometres, approximately 20]
-
A.
distanceFromBesançonKilometres
Indicates the distance, measured in kilometers, between an entity and the city of Besançon.
-
B.
distanceFromParisCenter
Indicates the measured distance between a given location and the central point of Paris.
-
C.
distanceFromParisSaintLazare
Indicates the physical distance between a given place and Paris Saint-Lazare railway station.
-
D.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
E.
distanceToMetzKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Metz.
- 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_69b34537cc6481909cd0a96acbb33ef7 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e72ff588190a50c04ab975612dd |
completed | March 12, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69b347f3bd188190b0cd613e8a5c1683 |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e04ef1c81908bb34ae1cbfab1e6 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:05 p.m.