Triple
T32654966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Memorial Van Damme |
E834835
|
entity |
| Predicate | languageOfHostCity |
P201292
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Memorial Van Damme, languageOfHostCity, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfHostCity Context triple: [Memorial Van Damme, languageOfHostCity, French]
-
A.
languageOfLocalOrganisingCommittee
Indicates the language used by the local organising committee to conduct its activities, communication, and official documentation.
-
B.
languageOfEvent
Indicates the language in which an event is conducted, presented, or communicated.
-
C.
languageOfSurroundingCountry
Indicates that a language is the primary or commonly used language in the country surrounding a given place or region.
-
D.
languageOfCompetition
Indicates the language in which a competition is conducted or officially presented.
-
E.
languageOfVenue
Indicates the language primarily used or officially designated for communication at a given venue.
- F. None of above. chosen
Provenance (4 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_69f3492f72248190ba42fa596aea50e1 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ffe6e2eb688190a45fd2c6415cd86a |
completed | May 10, 2026, 2:01 a.m. |
| PD | Predicate disambiguation | batch_69ffe65939488190a35b9c2e9c7ad868 |
completed | May 10, 2026, 1:58 a.m. |
| PDg | Predicate description generation | batch_69ffe6e21f748190a18dfe0878d397f7 |
completed | May 10, 2026, 2:01 a.m. |
Created at: May 1, 2026, 1:08 a.m.