Triple
T1187386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rue de Valois |
E25277
|
entity |
| Predicate | hasArrondissement |
P26130
|
FINISHED |
| Object | 1st arrondissement of Paris |
—
|
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: 1st arrondissement of Paris | Statement: [Rue de Valois, hasArrondissement, 1st arrondissement of Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasArrondissement Context triple: [Rue de Valois, hasArrondissement, 1st arrondissement of Paris]
-
A.
belongsToIntercommunality
Indicates that an entity is a member of, or administratively attached to, a specific intercommunal structure or grouping.
-
B.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
C.
targetCityDistrict
Indicates that one entity is a specific city district that serves as the target or destination in relation to another entity.
-
D.
hasNeighbourhood
Indicates that one entity is located within, or is associated with, a particular neighborhood area of another entity.
-
E.
isMunicipalHomeOf
Indicates that a municipality serves as the official home base or hosting location for a particular entity or organization.
- F. None of above. chosen
Provenance (4 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd5578b08190bbe4089857fbf166 |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5bacc481909e8dfd5215e4711a |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bd0ab5f88190bb583fc63b4cc150 |
completed | March 1, 2026, 10:26 p.m. |
Created at: March 1, 2026, 7:45 p.m.