Triple
T968717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Robber Bride |
E20896
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Tony |
E118947
|
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: Tony | Statement: [The Robber Bride, hasCharacter, Tony]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tony Context triple: [The Robber Bride, hasCharacter, Tony]
-
A.
Tony
The Tony is a prestigious American theater award presented annually to recognize excellence in Broadway productions.
-
B.
Tony
chosen
Tony is one of the central protagonists in Margaret Atwood’s novel "The Robber Bride," known for her intellectual, introspective nature and complex relationships with the other main characters.
-
C.
Ted
Ted is a masculine given name, often a diminutive of Theodore or Edward, commonly used in English-speaking countries.
-
D.
Ted
Ted is a 2012 comedy film about a foul-mouthed living teddy bear, created by and starring Seth MacFarlane.
-
E.
Tim
Tim is the given name of Tim Wu, a prominent legal scholar and policy advocate known for coining the term "net neutrality."
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4481f508190adcf0a965a23862c |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4287830c819095ffc30fc03a7461 |
completed | March 7, 2026, 3:21 p.m. |
Created at: March 1, 2026, 7:40 p.m.