Triple
T32187385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hinkley |
E822142
|
entity |
| Predicate | isBestKnownFor |
P22
|
FINISHED |
| Object | being the real-life setting of the events depicted in the film "Erin Brockovich" |
—
|
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: being the real-life setting of the events depicted in the film "Erin Brockovich" | Statement: [Hinkley, isBestKnownFor, being the real-life setting of the events depicted in the film "Erin Brockovich"]
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_69f3490819cc81909bae1f8ce99423c5 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6babc938c8190a233c3a8b5254802 |
completed | May 3, 2026, 3:02 a.m. |
Created at: May 1, 2026, 12:35 a.m.