Triple
T18819678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Twist |
E460229
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Sikes |
—
|
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: Sikes | Statement: [Twist, featuresCharacter, Sikes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sikes Context triple: [Twist, featuresCharacter, Sikes]
-
A.
Bill Sikes
chosen
Bill Sikes is a violent, menacing criminal and one of the primary antagonists in Charles Dickens's novel "Oliver Twist."
-
B.
Fagin
Fagin is a cunning, manipulative leader of a gang of child pickpockets in the musical "Oliver!", adapted from Charles Dickens' novel "Oliver Twist."
-
C.
Amos Slade
Amos Slade is the gruff, hot-tempered hunter who serves as the primary human antagonist in Disney’s animated film "The Fox and the Hound."
-
D.
Ripper Roo
Ripper Roo is a deranged, hyperactive kangaroo villain from the Crash Bandicoot video game series, known for his manic laughter and explosive attacks.
-
E.
Bartholomew Green
Bartholomew Green was a prominent early 18th-century Boston printer and publisher known for producing influential colonial American works.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6b8b7d88190a8828746176776ea |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 10, 2026, 11:55 a.m.