Triple
T26044297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vue de Saint-Tropez |
E647783
|
entity |
| Predicate | depictsLocationInCountry |
P8908
|
FINISHED |
| Object | France |
—
|
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: France | Statement: [Vue de Saint-Tropez, depictsLocationInCountry, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsLocationInCountry Context triple: [Vue de Saint-Tropez, depictsLocationInCountry, France]
-
A.
depictsCountry
Indicates that one entity visually represents or portrays a specific country.
-
B.
location depicted
chosen
Indicates that one entity visually represents or shows the place where another entity is situated or occurs.
-
C.
meetsInCountry
Indicates that two or more entities have an in-person meeting that takes place within the specified country.
-
D.
depictsFictionalPlace
Indicates that one entity visually represents or portrays a place that exists only in fiction rather than in the real world.
-
E.
isLocatedOn
Indicates that one entity exists at or is situated upon the surface or area of 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_69e77e8c88f08190858c4c81bd2e1b9a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6430a93a48190854ce71df680b2fa |
completed | May 2, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69f641da05b881909f6283c988639c53 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 22, 2026, 9:09 a.m.