Triple
T2770289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Wise |
E61438
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Wise |
E118203
|
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: Wise | Statement: [Robert Wise, familyName, Wise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wise Context triple: [Robert Wise, familyName, Wise]
-
A.
Wise
chosen
Wise is a surname shared by various notable individuals across fields such as entertainment, politics, and academia.
-
B.
Wise
Wise is a London-based financial technology company best known for its low-cost international money transfer and multi-currency account services.
-
C.
the Wise
The Wise is the honorific epithet of Frederick III, Elector of Saxony, renowned for protecting Martin Luther and playing a key role in the early Reformation.
-
D.
Wits
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.
-
E.
Wisdom
Wisdom is a common English surname borne by various notable individuals, including the British comedian and actor Norman Wisdom.
- 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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd690b24819095647dd4a4f902bb |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc05061808190abe709eff7a8c986 |
completed | March 10, 2026, 6:55 a.m. |
Created at: March 6, 2026, 9:57 p.m.