Triple
T11077739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National University of Tucumán |
E261910
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | UNT |
E549771
|
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: UNT | Statement: [National University of Tucumán, shortName, UNT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UNT Context triple: [National University of Tucumán, shortName, UNT]
-
A.
UNT
chosen
UNT is the commonly used acronym for the National University of Trujillo, a public higher education institution in Trujillo, Peru.
-
B.
UME
UME is Spain’s specialized military emergency unit responsible for rapid response to natural disasters, major accidents, and other civil emergencies.
-
C.
UCT
UCT is a leading public research university in Cape Town, South Africa, renowned as one of Africa’s top higher education institutions.
-
D.
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.
-
E.
UM
UM is the commonly used abbreviation for Finland’s Ministry for Foreign Affairs, the government body responsible for the country’s foreign policy and diplomatic relations.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7999407288190a901d4a2427a2102 |
completed | April 9, 2026, 12:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3c8d4f22881909fa3d094a19b0a14 |
completed | April 18, 2026, 6:09 p.m. |
Created at: April 8, 2026, 9:27 p.m.