Triple
T14173122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knight Riders |
E351260
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Tabitha King |
E48215
|
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: Tabitha King | Statement: [Knight Riders, hasCastMember, Tabitha King]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabitha King Context triple: [Knight Riders, hasCastMember, Tabitha King]
-
A.
Tabitha King
chosen
Tabitha King is an American author known for her novels and short stories, and as the wife of writer Stephen King.
-
B.
Tabitha Grant
Tabitha Grant is the daughter of British actor Hugh Grant and his former partner Tinglan Hong.
-
C.
Tabitha Stephens
Tabitha Stephens is the magically gifted daughter of Samantha and Darrin on the classic 1960s TV sitcom "Bewitched."
-
D.
Tabitha Lenox
Tabitha Lenox is a powerful and mischievous witch from the soap opera "Passions," known for her dark magic, eccentric personality, and close bond with her doll-turned-sidekick, Timmy.
-
E.
Tara King
Tara King is a fictional British secret agent and one of John Steed’s partners in the 1960s television series "The Avengers."
- 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_69d8278834a08190b0f1784e58d7b99c |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b5dcbc8190b0cfcce5e6c6d582 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf80a9b34819081c4ebf7429e875a |
completed | May 7, 2026, 8:37 p.m. |
Created at: April 10, 2026, 1:01 a.m.