Triple
T15339247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rouen metropolitan area |
E366746
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Bihorel |
E884437
|
NE 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: Bihorel | Statement: [Rouen metropolitan area, hasPart, Bihorel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bihorel Context triple: [Rouen metropolitan area, hasPart, Bihorel]
-
A.
Bihorel
chosen
Bihorel is a suburban commune in northern France, located near Rouen and integrated into its public transport network.
-
B.
Buthier
Buthier is a river in Italy’s Aosta Valley that flows through the city of Aosta before joining the Dora Baltea.
-
C.
Bédoin
Bédoin is a village in southeastern France’s Vaucluse department, best known as a classic starting point for cyclists ascending Mont Ventoux.
-
D.
Bongrand
Bongrand is a fictional character in Émile Zola’s novel *L’Œuvre*, depicted as an older, established painter who contrasts with the avant-garde ambitions of the protagonist.
-
E.
Bainouk
Bainouk is a lesser-known Niger-Congo language spoken by the Bainouk people primarily in Senegal and neighboring regions of West Africa.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e12eb7c8190944a260aa1aa9156 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01f2ee9c819080fce24ed13a07c7 |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:17 a.m.