Triple
T26774370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gilles Lellouche |
E670075
|
entity |
| Predicate | hasEnglishWikipediaPage |
P106464
|
FINISHED |
| Object | https://en.wikipedia.org/wiki/Gilles_Lellouche |
—
|
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: https://en.wikipedia.org/wiki/Gilles_Lellouche | Statement: [Gilles Lellouche, hasEnglishWikipediaPage, https://en.wikipedia.org/wiki/Gilles_Lellouche]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEnglishWikipediaPage Context triple: [Gilles Lellouche, hasEnglishWikipediaPage, https://en.wikipedia.org/wiki/Gilles_Lellouche]
-
A.
hasEnglishWikipediaArticle
chosen
Indicates that there exists an article about the subject in the English-language edition of Wikipedia.
-
B.
hasFrenchWikipediaPage
Indicates that the subject entity has a dedicated article on the French-language version of Wikipedia.
-
C.
usesWikidata
Indicates that one entity makes use of or relies on Wikidata as a data source or reference.
-
D.
hasOnlineEncyclopediaEntry
Indicates that an entity has a corresponding entry or article in an online encyclopedia.
-
E.
hasLanguageCodeOnWikipedia
Indicates that a particular language is represented on Wikipedia by a specific language code.
- 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_69eeb31c925881909b597f6e40056d28 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f67c9fe7b48190b79b4041357edb49 |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 27, 2026, 4:03 a.m.