Triple
T7662471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wits |
E173540
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Wits |
E173540
|
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: Wits | Statement: [Wits, shortName, Wits]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wits Context triple: [Wits, shortName, Wits]
-
A.
Wits
chosen
Wits is a major South African public research university based in Johannesburg, renowned for its strong academic programs and historical role in social and political activism.
-
B.
Wisen
Wisen is a small municipality in the canton of Solothurn in Switzerland, situated in a rural, hilly region near the border with the canton of Basel-Landschaft.
-
C.
WIT
WIT is the New York Stock Exchange ticker symbol for Wipro Limited, a major Indian multinational information technology services and consulting company.
-
D.
Witt
Witt is a surname most prominently associated with American television and film producer Paul Junger Witt, known for creating and producing numerous popular TV series.
-
E.
Wisser
The Wisser is a river in Germany that serves as a right-bank tributary of the Sieg.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a74a2c81909f78ab2de7ce807c |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b1aaef081908d1d181ea7c28c2f |
completed | March 29, 2026, 3:23 a.m. |
Created at: March 27, 2026, 3:59 p.m.