Triple
T3844777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lozère |
E93541
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Haute-Loire |
E31579
|
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: Haute-Loire | Statement: [Lozère, borders, Haute-Loire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haute-Loire Context triple: [Lozère, borders, Haute-Loire]
-
A.
Haute-Loire
chosen
Haute-Loire is a rural department in south-central France, known for its volcanic landscapes, the upper Loire River valley, and historic towns such as Le Puy-en-Velay.
-
B.
Drôme
Drôme is a department in southeastern France known for its diverse landscapes, historic towns, and location between the Alps and the Rhône Valley.
-
C.
Aveyron
Aveyron is a rural department in southern France known for its rugged landscapes, medieval villages, and traditional gastronomy including Roquefort cheese.
-
D.
Saône-et-Loire
Saône-et-Loire is a department in the Bourgogne-Franche-Comté region of eastern France, known for its historic towns, Romanesque churches, and Burgundy vineyards.
-
E.
Sarthe
Sarthe is a department in western France known for its capital Le Mans and the famous 24 Hours of Le Mans endurance race.
- 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_69aed96ce578819084ab16e3439976c9 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeebb655c081909ec5ff3d09eb4778 |
completed | March 9, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51c788a088190aa91261b83f21d86 |
completed | March 14, 2026, 8:29 a.m. |
Created at: March 9, 2026, 3:18 p.m.