Triple
T9928033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuks |
E187971
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | UP |
E187970
|
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: UP | Statement: [Tuks, hasAbbreviation, UP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UP Context triple: [Tuks, hasAbbreviation, UP]
-
A.
UP
UP is the standard reporting mark used to identify rail equipment owned or operated by the Union Pacific Railroad in North America.
-
B.
UP
chosen
UP is a leading South African public research university located in Pretoria, known for its comprehensive range of academic programs and strong research output.
-
C.
UP
UP is the Indian state of Uttar Pradesh, the country’s most populous state and a major political and cultural center in northern India.
-
D.
UP
UP is the commonly used abbreviation for the University of Pristina, a major public university in Kosovo.
-
E.
UP
UP was a major South African political party that dominated the country’s politics for much of the mid-20th century before being displaced by the National Party.
- 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_69ca82b22a688190b52c75bd48429c10 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb59d7ad08190982a1584547190bd |
completed | April 2, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d369657ecc81909d0c710ca919602e |
completed | April 6, 2026, 8:05 a.m. |
Created at: March 30, 2026, 8:43 p.m.