Triple
T8970359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montmorency |
E214248
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Domont |
E258293
|
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: Domont | Statement: [Montmorency, locatedNear, Domont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Domont Context triple: [Montmorency, locatedNear, Domont]
-
A.
Domont
chosen
Domont is a suburban commune in northern France located in the Val-d'Oise department within the Île-de-France region, forming part of the greater Paris metropolitan area.
-
B.
Demonte
Demonte is a small historic town in Italy’s Piedmont region, situated in the Stura di Demonte Valley in the Cuneo province.
-
C.
Dominyk
Dominyk is a given name, typically a modern or stylized variant of the name Dominik.
-
D.
Doncieux
Doncieux is a French surname most notably associated with Camille Doncieux, the first wife and frequent model of painter Claude Monet.
-
E.
Rhume
The Rhume is a river in Lower Saxony, Germany, known as a tributary of the Leine and for its karstic spring source near the Harz mountains.
- 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_69ca839dbf608190a2f5990477115d29 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc67672c108190919ae6ca69b6291f |
completed | April 1, 2026, 12:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc96006e48190978e4ccdedc48b41 |
completed | April 3, 2026, 2:06 p.m. |
Created at: March 30, 2026, 7:02 p.m.