Triple
T10228427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parc de la Mairie |
E243273
|
entity |
| Predicate | nearbyAdministrativeCenter |
P34698
|
FINISHED |
| Object | Chêne-Bougeries town hall |
—
|
LITERAL 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: Chêne-Bougeries town hall | Statement: [Parc de la Mairie, nearbyAdministrativeCenter, Chêne-Bougeries town hall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyAdministrativeCenter Context triple: [Parc de la Mairie, nearbyAdministrativeCenter, Chêne-Bougeries town hall]
-
A.
administrativeCentreNearby
chosen
Indicates that an administrative centre is located close to the referenced entity in geographic or spatial terms.
-
B.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
C.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
D.
nearestTownCenter
Indicates that one location is the closest town center to another specified point or area.
-
E.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
- F. None of above.
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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23b620c8190b8a72d0eb0d16b93 |
completed | April 7, 2026, 9:45 a.m. |
| PD | Predicate disambiguation | batch_69d4d1e9798c8190b437d53d48554ba1 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:18 a.m.