Triple
T31030039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ailly-sur-Somme |
E790695
|
entity |
| Predicate | distanceToAmiensKilometers |
P117652
|
FINISHED |
| Object | approximately 10 |
—
|
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 10 | Statement: [Ailly-sur-Somme, distanceToAmiensKilometers, approximately 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAmiensKilometers Context triple: [Ailly-sur-Somme, distanceToAmiensKilometers, approximately 10]
-
A.
distanceToAmiens
chosen
Indicates the spatial distance between a given entity’s location and the city of Amiens.
-
B.
distanceFromAmiens
Indicates the spatial distance between a given entity and the location Amiens.
-
C.
distanceToRouen
Indicates the spatial distance between a given entity and the location of Rouen.
-
D.
distanceFromAngersKilometres
Indicates the physical distance, measured in kilometers, between an entity and the location of Angers.
-
E.
distanceToBeauvaisKilometres
Indicates the physical distance, measured in kilometers, between a given entity and Beauvais.
- 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_69f224c97a788190b5da1ead6038a74e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a01926e2f348190a632eff5c91db5e0 |
completed | May 11, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_6a01923488f4819094d79a27f4bc8ab8 |
completed | May 11, 2026, 8:24 a.m. |
Created at: April 29, 2026, 8:59 p.m.