Triple
T14949684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayenne |
E372757
|
entity |
| Predicate | bordersDepartment |
P224
|
FINISHED |
| Object | Orne |
E123324
|
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: Orne | Statement: [Mayenne, bordersDepartment, Orne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orne Context triple: [Mayenne, bordersDepartment, Orne]
-
A.
Orne
chosen
Orne is a rural department in northwestern France known for its pastoral landscapes, horse breeding, and historic towns such as Alençon.
-
B.
Olne
Olne is a small municipality in the province of Liège in Wallonia, eastern Belgium, known for its rural character and traditional village charm.
-
C.
The Nore
The Nore is a sandbank at the mouth of the Thames Estuary in England that historically served as a major Royal Navy anchorage and site of naval command.
-
D.
Møse
Møse is a Norwegian surname most notably borne by Erik Møse, a prominent jurist and international judge.
-
E.
Oron
Oron is a coastal ethnic group of southeastern Nigeria, known for its fishing traditions, distinct language, and cultural ties to neighboring Ibibio and Annang peoples.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded68fae3c81909873b113bfcaca05 |
completed | April 15, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e986dfc8190a5cf363dabe6bdef |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:39 a.m.