Triple
T9579340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Fat Liar |
E231128
|
entity |
| Predicate | plotSummary |
P264
|
FINISHED |
| Object | A teenager travels to Hollywood to prove that a movie producer stole his story and turned it into a hit film. |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: A teenager travels to Hollywood to prove that a movie producer stole his story and turned it into a hit film. | Statement: [Big Fat Liar, plotSummary, A teenager travels to Hollywood to prove that a movie producer stole his story and turned it into a hit film.]
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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99aece1081908287e03106de020f |
completed | April 1, 2026, 10:18 p.m. |
Created at: March 30, 2026, 8:05 p.m.