Triple
T12942904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Misfit |
E309679
|
entity |
| Predicate | notableQuote |
P492
|
FINISHED |
| Object | “She would of been a good woman if it had been somebody there to shoot her every minute of her life.” |
—
|
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: “She would of been a good woman if it had been somebody there to shoot her every minute of her life.” | Statement: [The Misfit, notableQuote, “She would of been a good woman if it had been somebody there to shoot her every minute of her life.”]
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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e1a28688190ab9fd1307bc76b4a |
completed | April 10, 2026, 10:47 p.m. |
Created at: April 9, 2026, 5:43 p.m.