Triple
T2312835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bourbon-l’Archambault |
E50996
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Vichy |
E1394
|
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: Vichy | Statement: [Bourbon-l’Archambault, nearbyCity, Vichy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vichy Context triple: [Bourbon-l’Archambault, nearbyCity, Vichy]
-
A.
Vichy
chosen
Vichy is a spa town in central France renowned for its thermal springs, health resorts, and role as the seat of the World War II Vichy regime.
-
B.
Compiegne
Compiègne is a historic city in northern France known for its royal château, forest, and role in significant events such as the signing of the 1918 Armistice.
-
C.
Thoiry
Thoiry is a commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
-
D.
Limoges
Limoges is a historic city in central France renowned for its fine porcelain production and medieval architecture.
-
E.
Évian-les-Bains
Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc61a8e248190b5024cca9efd806d |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae895f5420819087b403e9772dce9a |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.