Triple
T27262595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamil Nadu Engineering Admissions |
E687806
|
entity |
| Predicate | coversProgram |
P171422
|
FINISHED |
| Object | B.E. |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
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: B.E. | Statement: [Tamil Nadu Engineering Admissions, coversProgram, B.E.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversProgram Context triple: [Tamil Nadu Engineering Admissions, coversProgram, B.E.]
-
A.
coversFrom
Indicates that one entity provides protection, concealment, or shelter for another entity against something originating from a specified source or direction.
-
B.
coversSection
Indicates that one entity includes, addresses, or provides content for a particular section of another entity.
-
C.
coversEvent
Indicates that one event includes, spans, or encompasses the time period or occurrence of another event.
-
D.
coversTo
Indicates that one entity extends its coverage or protective scope to include another entity.
-
E.
supportsProgramOf
Indicates that one entity provides assistance, resources, or endorsement to help another entity’s program operate or succeed.
- F. None of above. chosen
Provenance (4 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_69ef3557abc481908bf3c146f0f3356a |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f69f80b62c8190bf2af2be0d3a7df8 |
completed | May 3, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69f69d17e8d48190b30bcc2f4bd81eb2 |
completed | May 3, 2026, 12:55 a.m. |
| PDg | Predicate description generation | batch_69f69edae2448190925ce701c8792c52 |
completed | May 3, 2026, 1:03 a.m. |
Created at: April 27, 2026, 10:53 a.m.