Triple
T18860535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurent Landi |
E461299
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Cecile Landi |
—
|
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: Cecile Landi | Statement: [Laurent Landi, spouse, Cecile Landi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cecile Landi Context triple: [Laurent Landi, spouse, Cecile Landi]
-
A.
Cecile Landi
chosen
Cecile Landi is a prominent French-American gymnastics coach best known for coaching Olympic champion Simone Biles and other elite U.S. gymnasts.
-
B.
Lydia Krige
Lydia Krige was the wife of South African poet, writer, and translator Uys Krige.
-
C.
Patricia de Lille
Patricia de Lille is a South African politician and former mayor of Cape Town known for her long-standing role in national and local government and for founding the Independent Democrats party.
-
D.
Charlotte Cantilini
Charlotte Cantilini is the sweet-natured but determined heroine of the romantic comedy film "Monster-in-Law," who struggles to win over her overbearing future mother-in-law.
-
E.
Yvonne Zima
Yvonne Zima is an American actress known for her work as a child performer in films and television, including notable roles in 1990s thrillers and popular TV dramas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c060bfc4819092ac591692a6ccd5 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 10, 2026, 11:57 a.m.