Triple
T12630105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cher (department) |
E301616
|
entity |
| Predicate | bordersDepartment |
P224
|
FINISHED |
| Object | Indre |
E241347
|
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: Indre | Statement: [Cher (department), bordersDepartment, Indre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Indre Context triple: [Cher (department), bordersDepartment, Indre]
-
A.
Indre
Indre is a river in central France that flows through the regions of Berry and Touraine before joining the Loire.
-
B.
Indre
chosen
Indre is a rural department in central France known for its quiet countryside, historic towns, and location within the Centre-Val de Loire region.
-
C.
Endre
Endre is the Hungarian given name of film director André De Toth, known for his work in mid-20th-century cinema.
-
D.
Innlandet
Innlandet is a county in eastern Norway known for its inland landscapes, including mountains, forests, and important winter sports venues.
-
E.
Innlandet
Innlandet is an island district of the Norwegian town of Kristiansund, known for its traditional wooden houses and coastal maritime character.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9610ce9e48190a496824002c0d2be |
completed | April 10, 2026, 8:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65edcdd208190893b3046c3e9d327 |
completed | May 2, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:15 p.m.