Triple
T6836291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yogyakarta State University |
E157458
|
entity |
| Predicate | acronym |
P43
|
FINISHED |
| Object | UNY |
E622185
|
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: UNY | Statement: [Yogyakarta State University, acronym, UNY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UNY Context triple: [Yogyakarta State University, acronym, UNY]
-
A.
UNY
chosen
UNY is an Indonesian public university in Yogyakarta known for its strong focus on teacher education and educational sciences.
-
B.
UNI
UNI is a public university in Cedar Falls, Iowa, known for its strong teacher education programs and comprehensive undergraduate and graduate offerings.
-
C.
UNI
UNI was a 1960s–1970s American record label and imprint of MCA Records known for releasing rock, pop, and soul music.
-
D.
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.
-
E.
UNA
UNA is a public university located in Florence, Alabama, known for its regional academic programs and historic campus.
- 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_69c6882c53608190b99aebef079b23bd |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d67c1c508190ab39b8aaaaacc628 |
completed | March 27, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72fb02e0c8190ae1514875e03208a |
completed | March 28, 2026, 1:32 a.m. |
Created at: March 27, 2026, 2:19 p.m.