Triple
T33910043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fondation Brigitte Bardot |
E869288
|
entity |
| Predicate | hasSheltersFor |
P3789
|
FINISHED |
| Object | domestic animals |
—
|
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: domestic animals | Statement: [Fondation Brigitte Bardot, hasSheltersFor, domestic animals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSheltersFor Context triple: [Fondation Brigitte Bardot, hasSheltersFor, domestic animals]
-
A.
hasShelters
chosen
Indicates that one entity provides, contains, or is associated with one or more shelters for another entity or purpose.
-
B.
areaServedAsShelterFor
Indicates that one entity functioned as a shelter or refuge for another entity, providing protection or a safe place.
-
C.
shelterType
Indicates the kind or category of shelter associated with an entity (e.g., tent, house, bunker).
-
D.
hasShelteredAreas
Indicates that one entity provides or contains areas that offer protection or cover for another entity.
-
E.
numberOfPaintedShelters
Indicates the count of shelters that have been painted in the given context.
- 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_69f3499869bc8190b6c33a81686af226 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 1, 2026, 1:48 a.m.