Triple
T29600383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheb Castle |
E754424
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | heritage and museum authorities of the town of Cheb |
—
|
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: heritage and museum authorities of the town of Cheb | Statement: [Cheb Castle, operator, heritage and museum authorities of the town of Cheb]
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_69f0ef84e5d08190a0df17f5930ceed3 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69f66dbae46881909b6543de2bd2f273 |
completed | May 2, 2026, 9:33 p.m. |
Created at: April 28, 2026, 6:21 p.m.