Triple
T22833363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faulk |
E565868
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Faulks |
—
|
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: Faulks | Statement: [Faulk, hasVariant, Faulks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Faulks Context triple: [Faulk, hasVariant, Faulks]
-
A.
Faulks
chosen
Faulks is the surname of British novelist and journalist Sebastian Faulks, best known for his historical and literary fiction.
-
B.
Foulkes
Foulkes is an English surname most notably associated with Bill Foulkes, a long-serving Manchester United and England footballer.
-
C.
Falkner
Falkner is a lesser-known 1837 novel by Mary Shelley that explores themes of guilt, redemption, and complex family relationships.
-
D.
Fowles
Fowles is the surname of Sylvia Fowles, an American professional basketball player renowned as one of the most dominant centers in WNBA history.
-
E.
Figes
Figes is a surname most notably associated with British historian and author Orlando Figes, known for his works on Russian and European history.
- 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_69e24585ab1c81909b2b5065d15805d5 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e2d830881908d69929b854aad66 |
completed | April 29, 2026, 3:42 a.m. |
Created at: April 17, 2026, 3:35 p.m.