Triple
T11844581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francis |
E281740
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | François |
E936028
|
NE 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: François | Statement: [Francis, hasVariant, François]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: François Context triple: [Francis, hasVariant, François]
-
A.
François
François is the given name of the French poet and essayist Sully Prudhomme, the first recipient of the Nobel Prize in Literature.
-
B.
François
François is a central character in Claude Chabrol’s 1958 French New Wave film "Le Beau Serge," whose troubled life and relationships drive much of the drama.
-
C.
François
François is the French given name of Francis Carco, a 20th-century French novelist, poet, and journalist known for his portrayals of Parisian underworld life.
-
D.
François
chosen
François is a French given name historically borne by notable figures such as Marshal Luxembourg, reflecting its long-standing prominence in Francophone cultures.
-
E.
Francois
Francois is the given first name of South African rugby union scrum-half Faf de Klerk.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ab287ba48190a5178779fd19b9b7 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a65b5ff08190bb58361f6a6acdca |
completed | April 10, 2026, 7:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f1679729c08190a9f6750586f90d8d |
completed | April 29, 2026, 2:06 a.m. |
Created at: April 8, 2026, 9:43 p.m.