Triple
T14713188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Natalie Clifford Barney |
E345603
|
entity |
| Predicate | locationOfSalons |
P94237
|
FINISHED |
| Object | 20, rue Jacob, Paris, France |
—
|
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: 20, rue Jacob, Paris, France | Statement: [Natalie Clifford Barney, locationOfSalons, 20, rue Jacob, Paris, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locationOfSalons Context triple: [Natalie Clifford Barney, locationOfSalons, 20, rue Jacob, Paris, France]
-
A.
typeOfSalon
Indicates the specific category or kind of salon that an entity is classified as.
-
B.
aimOfSalon
Indicates that a particular goal, purpose, or objective is the intended focus or mission of a salon.
-
C.
maintainedSalonIn
chosen
Indicates that an entity operated or hosted a salon (a regular social or intellectual gathering) in a particular location.
-
D.
nearbyVenue
Indicates that one venue is located close to another venue in physical space.
-
E.
findingOnFacilities
Indicates a relationship where an inspection or assessment identifies a specific issue, observation, or result associated with particular facilities.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb982bf248190881e21a8a0861a3f |
completed | April 14, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.