Triple
T230927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | canton of Geneva |
E4408
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Confignon |
E31639
|
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: Confignon | Statement: [canton of Geneva, hasMunicipality, Confignon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Confignon Context triple: [canton of Geneva, hasMunicipality, Confignon]
-
A.
Anjou
Anjou is a historic region in western France that was once a powerful medieval county and later a duchy, playing a central role in the Angevin Empire and European dynastic politics.
-
B.
Mouton
Mouton is an academic publishing house known for its influential works in linguistics and related fields.
-
C.
Eygues
Eygues is a river in southeastern France that flows through the Drôme department before joining the larger Rhône basin.
-
D.
Thônex
chosen
Thônex is a municipality in western Switzerland that forms part of the suburban area of Geneva near the French border.
-
E.
Burgundy
Burgundy is a renowned wine-producing region in eastern France, famous for its high-quality Chardonnay and Pinot Noir wines.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25cadae1c8190be0e8dcf33351187 |
completed | Feb. 28, 2026, 3:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3736e4010819089000ed60dbf519c |
completed | Feb. 28, 2026, 10:59 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.