Triple
T15180743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carros |
E362735
|
entity |
| Predicate | distanceToNiceKilometers |
P117025
|
FINISHED |
| Object | approximately 17 |
—
|
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 17 | Statement: [Carros, distanceToNiceKilometers, approximately 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToNiceKilometers Context triple: [Carros, distanceToNiceKilometers, approximately 17]
-
A.
distanceToMetzKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Metz.
-
B.
distanceToMarseilleKilometers
Indicates the physical distance, measured in kilometers, between a given location or entity and the city of Marseille.
-
C.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
D.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
-
E.
distanceToMeauxKilometersApprox
Indicates the approximate distance, measured in kilometers, between a given location and Meaux.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00664caac81909bee1268264769f8 |
completed | April 15, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69deb97bd8bc8190b2ad4888f97cf963 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec72059c08190a34f513a00185b08 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:09 a.m.