Triple
T28200517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vice-Chancellor of Himachal Pradesh University |
E716869
|
entity |
| Predicate | goal |
P68
|
FINISHED |
| Object | development of university infrastructure |
—
|
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: development of university infrastructure | Statement: [Vice-Chancellor of Himachal Pradesh University, goal, development of university infrastructure]
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_69efd6b826908190857e6e7dad74ed93 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69f642d510548190b9f34ed50d80f858 |
completed | May 2, 2026, 6:30 p.m. |
Created at: April 27, 2026, 10:31 p.m.