Triple
T26043898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evergreen Cemetery |
E647770
|
entity |
| Predicate | transportNearby |
P5822
|
FINISHED |
| Object | Eastside Los Angeles transit lines |
—
|
NE NERFINISHED |
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: Eastside Los Angeles transit lines | Statement: [Evergreen Cemetery, transportNearby, Eastside Los Angeles transit lines]
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_69e77e8c88f08190858c4c81bd2e1b9a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f606588684819087b366450d7c4fa9 |
completed | May 2, 2026, 2:12 p.m. |
Created at: April 22, 2026, 9:09 a.m.