Triple
T11067543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cour Saint-Émilion |
E261663
|
entity |
| Predicate | hasCommercialAreaNearby |
P19783
|
FINISHED |
| Object | shops |
—
|
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: shops | Statement: [Cour Saint-Émilion, hasCommercialAreaNearby, shops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialAreaNearby Context triple: [Cour Saint-Émilion, hasCommercialAreaNearby, shops]
-
A.
connectsToCommercialArea
Indicates that one location has a direct link, route, or access path to a commercial area.
-
B.
hasMajorCompanyNearby
Indicates that a location or entity is situated close to at least one large or significant company.
-
C.
hasNearbyLandUse
chosen
Indicates that one land area is located close to another area characterized by a specific type of land use.
-
D.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
E.
nearbyEconomicActivity
Indicates that there is economic activity occurring in close physical proximity to the referenced 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79920428c81908db824ab54e08e8d |
completed | April 9, 2026, 12:18 p.m. |
| PD | Predicate disambiguation | batch_69d74411d9e881908c0eeafa0f38e4b6 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.