Triple
T22092740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tana French |
E545949
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tana French |
—
|
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: Tana French | Statement: [Tana French, name, Tana French]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tana French Context triple: [Tana French, name, Tana French]
-
A.
Tana French
chosen
Tana French is an acclaimed Irish-based crime novelist best known for her psychologically rich Dublin Murder Squad series.
-
B.
Elly Griffiths
Elly Griffiths is a British crime novelist best known for her Ruth Galloway mystery series and other popular detective fiction.
-
C.
Val McDermid
Val McDermid is a Scottish crime writer renowned for her psychological thrillers and influential contributions to contemporary crime fiction.
-
D.
Lindsey Davis
Lindsey Davis is a British historical crime novelist best known for her Marcus Didius Falco series set in ancient Rome.
-
E.
Denise Mina
Denise Mina is a Scottish crime writer acclaimed for her gritty, socially aware novels and contributions to contemporary crime fiction.
- 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_69e11e36d03c8190a83a1ba802b7231b |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128e6b1d881909bf0f4a52199354c |
completed | April 28, 2026, 9:38 p.m. |
Created at: April 16, 2026, 8:29 p.m.