Triple
T15113453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Mississippi |
E360972
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | UM |
E360972
|
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: UM | Statement: [University of Mississippi, shortName, UM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UM Context triple: [University of Mississippi, shortName, UM]
-
A.
UM
UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
-
B.
UM
UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
-
C.
UM
UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
-
D.
UM
chosen
UM is a public research university in Oxford, Mississippi, commonly known as "Ole Miss" and recognized for its academic programs and SEC athletics.
-
E.
UM
UM is the commonly used abbreviation for Universitas Negeri Malang, a public university in Malang, Indonesia known for its strong focus on education and teacher training.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058d786c8190937c6819255c01bd |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69febfe2369881908c7ebbad412d9000 |
completed | May 9, 2026, 5:02 a.m. |
Created at: April 10, 2026, 3:05 a.m.