Triple
T12252279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airlangga University |
E292002
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | UNAIR |
E292002
|
NE 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: UNAIR | Statement: [Airlangga University, shortName, UNAIR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UNAIR Context triple: [Airlangga University, shortName, UNAIR]
-
A.
UNAIR
chosen
UNAIR is the commonly used abbreviation for Airlangga University, a major public university located in Surabaya, Indonesia.
-
B.
UNAP
UNAP is a public university in Peru’s Amazon region known for its focus on tropical ecology, environmental studies, and regional development.
-
C.
UNA
UNA is the stock ticker symbol for Unilever, a major multinational consumer goods company known for its wide range of food, personal care, and household products.
-
D.
UNA
UNA is a public university located in Florence, Alabama, known for its regional academic programs and historic campus.
-
E.
UNA
UNA is the commonly used acronym for the National University of Costa Rica, a major public higher education and research institution in the country.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cc849308190b6ff416f8b4f01e8 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60abbf75c81908a25e1c0a4aee8c1 |
completed | May 2, 2026, 2:31 p.m. |
Created at: April 8, 2026, 9:52 p.m.