Triple
T21462674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emma Peel |
E529510
|
entity |
| Predicate | successorCharacter |
P129151
|
FINISHED |
| Object | Tara King |
—
|
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: Tara King | Statement: [Emma Peel, successorCharacter, Tara King]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tara King Context triple: [Emma Peel, successorCharacter, Tara King]
-
A.
Tara King
chosen
Tara King is a fictional British secret agent and one of John Steed’s partners in the 1960s television series "The Avengers."
-
B.
Jessica King
Jessica King is a character in the supernatural thriller film "The Gift," involved in the mysterious events surrounding a small-town community.
-
C.
Nicole King
Nicole King is a film producer known for her work on the family comedy movie "Yes Day."
-
D.
Laura King
Laura King is the sister of British singer and former The Saturdays member Mollie King.
-
E.
Nina King
Nina King is an American college athletics administrator who serves as the athletic director at Duke University, overseeing the university’s sports programs.
- 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_69e0c458133481908ae8b41a12c4edec |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9efdb188190be79b72e1bd18860 |
completed | April 23, 2026, 9:44 a.m. |
Created at: April 16, 2026, 6:09 p.m.