Triple
T2505170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cantal |
E52560
|
entity |
| Predicate | bordersDepartment |
P224
|
FINISHED |
| Object | Cantal |
E52560
|
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: Cantal | Statement: [Cantal, bordersDepartment, Cantal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cantal Context triple: [Cantal, bordersDepartment, Cantal]
-
A.
Cantal
chosen
Cantal is a rural department in south-central France known for its volcanic landscapes, pastoral agriculture, and the production of Cantal cheese.
-
B.
Osona
Osona is a historical inland comarca in Catalonia, Spain, known for its rural landscapes, medieval towns, and the city of Vic as its main urban center.
-
C.
Bega
Bega is a rural town in New South Wales, Australia, best known as a major dairy and cheese-producing centre.
-
D.
Anjou
Anjou is a residential borough in the eastern part of Montreal, Quebec, known for its suburban character and shopping centers.
-
E.
Anjou
Anjou is a historic region in western France that was once a powerful medieval county and later a duchy, playing a central role in the Angevin Empire and European dynastic politics.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1cec9f48190848b6129aa394ce4 |
completed | March 7, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1fa57aa4819096578a5538973ec4 |
completed | March 9, 2026, 7:29 p.m. |
Created at: March 6, 2026, 9:46 p.m.