Triple
T12521823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pas de Peyrol |
E299336
|
entity |
| Predicate | connectsSettlement |
P8239
|
FINISHED |
| Object | Le Falgoux |
E993609
|
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: Le Falgoux | Statement: [Pas de Peyrol, connectsSettlement, Le Falgoux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Le Falgoux Context triple: [Pas de Peyrol, connectsSettlement, Le Falgoux]
-
A.
Le Falgoux
chosen
Le Falgoux is a small rural commune in the Cantal department of south-central France, nestled in the Auvergne volcanic mountains.
-
B.
Le Roeulx
Le Roeulx is a historic town in the province of Hainaut in Wallonia, Belgium, known for its castle, traditional architecture, and proximity to the Canal du Centre.
-
C.
Le Guignon
Le Guignon is a poem by French writer Charles Baudelaire, included among his early poetic works.
-
D.
La Bessiere
La Bessiere is a key character in the 1930 romantic drama film "Morocco," involved in the central love triangle alongside the leads.
-
E.
Le Brusquet
Le Brusquet is a small commune in southeastern France situated in the Alpes-de-Haute-Provence department.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9545b2b2481909049a490c97678f2 |
completed | April 10, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6685d8c64819088db715d7552d18d |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:57 p.m.