Triple
T30486731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Government Engineering College Thrissur |
E775739
|
entity |
| Predicate | offersResearchOpportunities |
P128291
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Government Engineering College Thrissur, offersResearchOpportunities, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersResearchOpportunities Context triple: [Government Engineering College Thrissur, offersResearchOpportunities, yes]
-
A.
offersResearch
chosen
Indicates that one entity provides or makes available research opportunities, resources, or services to another entity.
-
B.
offersStudyAbroad
Indicates that an institution or program provides opportunities for participants to study in a foreign country.
-
C.
offersOpportunity
Indicates that one entity provides another with a chance or possibility to do, obtain, or experience something.
-
D.
offersFieldOfStudy
Indicates that an institution or program provides a particular field of study as an available area of academic focus.
-
E.
offersExchangeOpportunities
Indicates that one entity provides chances for another entity to participate in exchanges, such as trading, swapping, or reciprocal programs.
- F. None of above.
Provenance (3 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_69f22497f91c8190afa7165bc900accd |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fcf1b3d9a08190850b388308656266 |
completed | May 7, 2026, 8:10 p.m. |
| PD | Predicate disambiguation | batch_69fcf0226d8c8190b23dceafb1794995 |
completed | May 7, 2026, 8:03 p.m. |
Created at: April 29, 2026, 8:13 p.m.