Triple
T17034079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Val-de-Reuil |
E413273
|
entity |
| Predicate | distanceToRouenKilometres |
P92618
|
FINISHED |
| Object | approximately 30 |
—
|
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 30 | Statement: [Val-de-Reuil, distanceToRouenKilometres, approximately 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToRouenKilometres Context triple: [Val-de-Reuil, distanceToRouenKilometres, approximately 30]
-
A.
distanceToRouen
chosen
Indicates the spatial distance between a given entity and the location of Rouen.
-
B.
distanceToLeHavre
Indicates the spatial distance between a given entity and the location of Le Havre.
-
C.
distanceFromCalais
Indicates the measured distance separating a given place or object from the location of Calais.
-
D.
distanceFromAngersKilometres
Indicates the physical distance, measured in kilometers, between an entity and the location of Angers.
-
E.
distanceToMarseilleKilometers
Indicates the physical distance, measured in kilometers, between a given location or entity and the city of Marseille.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d8eea4448190bd2eed88de2b4e73 |
completed | April 18, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69e35d5be7f48190af9db67a1e23850f |
completed | April 18, 2026, 10:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.