Triple
T1695220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cher |
E36641
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Indre |
E61705
|
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, borders, Indre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Indre Context triple: [Cher, borders, Indre]
-
A.
Indre
chosen
Indre is a river in central France that flows through the regions of Berry and Touraine before joining the Loire.
-
B.
Innlandet
Innlandet is a county in eastern Norway known for its inland landscapes, including mountains, forests, and important winter sports venues.
-
C.
Vestre
Vestre is a Norwegian surname most notably associated with Jan Christian Vestre, a prominent Norwegian politician and businessman.
-
D.
Emmen
Emmen is a major town and economic center in the northeastern Netherlands, known for its modern urban layout and attractions such as the Wildlands Adventure Zoo.
-
E.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
- 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_69a886163dec8190859c514232a37a05 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa62b3b8908190afc3f9e4a384684f |
completed | March 6, 2026, 5:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad7998e1108190aa7430cd4ef887d9 |
completed | March 8, 2026, 1:28 p.m. |
Created at: March 4, 2026, 7:30 p.m.