Triple
T15265535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Conflent |
E364891
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Mont-Louis |
E1010971
|
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: Mont-Louis | Statement: [Conflent, containsTown, Mont-Louis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mont-Louis Context triple: [Conflent, containsTown, Mont-Louis]
-
A.
Mont-Louis
chosen
Mont-Louis is a fortified town in the French Pyrenees renowned for its well-preserved 17th-century military architecture designed by the engineer Vauban.
-
B.
Mont Dauban
Mont Dauban is the tallest mountain on Silhouette Island in the Seychelles, known for its lush tropical forests and panoramic views over the Indian Ocean.
-
C.
Mont-Oriol
Mont-Oriol is a novel by French writer Guy de Maupassant that satirically explores love, ambition, and the commercialization of a spa town in 19th-century France.
-
D.
Montbéliarde
Montbéliarde is a French dairy cattle breed from the Franche-Comté region, valued for its high-quality milk used in traditional cheeses.
-
E.
Mont des Avaloirs
Mont des Avaloirs is a prominent hill in northwestern France known as the highest summit of the Armorican Massif and a notable regional viewpoint.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00851c5b88190a296b6a105d3ee30 |
completed | April 15, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee600340c8190a1888d35c2c1bc86 |
completed | May 9, 2026, 7:45 a.m. |
Created at: April 10, 2026, 3:14 a.m.