Triple
T4566753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UniNE |
E121927
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | UniNE |
E121927
|
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: UniNE | Statement: [UniNE, shortName, UniNE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UniNE Context triple: [UniNE, shortName, UniNE]
-
A.
UniNE
chosen
UniNE is a Swiss public university located in Neuchâtel, known for its research and teaching in fields such as law, economics, humanities, and natural sciences.
-
B.
UniFR
UniFR is the commonly used abbreviation for the University of Fribourg, a bilingual (French and German) public university in Fribourg, Switzerland.
-
C.
UNI
UNI is a public university in Cedar Falls, Iowa, known for its strong teacher education programs and comprehensive undergraduate and graduate offerings.
-
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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd589e35808190aa609bb04b128dbe |
completed | March 20, 2026, 2:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdd3b560a08190a485e9ec45e0f0f8 |
completed | March 20, 2026, 11:09 p.m. |
Created at: March 20, 2026, 1:09 p.m.