Triple
T15007251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rum Hospital |
E377739
|
entity |
| Predicate | significance |
P428
|
FINISHED |
| Object | example of early colonial public works financed by trade monopoly |
—
|
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: example of early colonial public works financed by trade monopoly | Statement: [Rum Hospital, significance, example of early colonial public works financed by trade monopoly]
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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded73348d4819091d9e7f1b0fed822 |
completed | April 15, 2026, 12:09 a.m. |
Created at: April 10, 2026, 2:55 a.m.