Triple
T21842426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detective Richard Willis |
E539286
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Camille Preaker |
—
|
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: Camille Preaker | Statement: [Detective Richard Willis, associatedWith, Camille Preaker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Camille Preaker Context triple: [Detective Richard Willis, associatedWith, Camille Preaker]
-
A.
Camille Preaker
chosen
Camille Preaker is the troubled, self-harming journalist protagonist of Gillian Flynn’s novel "Sharp Objects," who returns to her hometown to investigate a series of murders while confronting her traumatic past.
-
B.
Camille
Camille is a 1921 American silent drama film adaptation of Alexandre Dumas fils' novel "La Dame aux Camélias."
-
C.
Camille
Camille is a classic 1936 romantic drama film starring Greta Garbo as a tragic Parisian courtesan.
-
D.
Camille
Camille is a French given name used for both males and females, historically associated with figures such as the revolutionary journalist Camille Desmoulins.
-
E.
Camille
Camille is a character in Tennessee Williams’ play "Camino Real," a dreamlike drama set in a surreal, decaying town.
- 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_69e0c476c3c88190a92d08ebb59a128a |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0a7ad76d48190a1905cfdbe866323 |
completed | April 28, 2026, 12:27 p.m. |
Created at: April 16, 2026, 6:55 p.m.