Triple
T24900605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rue d’Anjou |
E623565
|
entity |
| Predicate | nearbyLandmarkArea |
P61270
|
FINISHED |
| Object | Rue du Faubourg Saint-Honoré |
—
|
NE NERFINISHED |
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: Rue du Faubourg Saint-Honoré | Statement: [Rue d’Anjou, nearbyLandmarkArea, Rue du Faubourg Saint-Honoré]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyLandmarkArea Context triple: [Rue d’Anjou, nearbyLandmarkArea, Rue du Faubourg Saint-Honoré]
-
A.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
B.
nearbyLocation
chosen
Indicates that one location is situated close to another location in physical space.
-
C.
infrastructureNearby
Indicates that one entity is located close to another entity that serves as infrastructure (such as roads, utilities, or public facilities).
-
D.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
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_69e2fac797cc8190b30d77f4121099ac |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f61f12b0f08190bc4a16907941864c |
completed | May 2, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69f61b37a5648190b10d33ae205ccfee |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 18, 2026, 5:27 a.m.