Triple
T10731080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoo Soon-taek |
E253074
|
entity |
| Predicate | otherName |
P39
|
FINISHED |
| Object | Madame Ban Ki-moon |
E9341
|
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: Madame Ban Ki-moon | Statement: [Yoo Soon-taek, otherName, Madame Ban Ki-moon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madame Ban Ki-moon Context triple: [Yoo Soon-taek, otherName, Madame Ban Ki-moon]
-
A.
Nane Maria Annan
Nane Maria Annan is a Swedish lawyer and artist best known as the widow of former UN Secretary-General Kofi Annan and for her work in human rights and humanitarian causes.
-
B.
Ban Ki-moon
chosen
Ban Ki-moon is a South Korean diplomat who served as the eighth Secretary-General of the United Nations from 2007 to 2016.
-
C.
Gabriele Annan
Gabriele Annan was a German-born British literary critic and editor known for her influential book reviews and contributions to major British publications.
-
D.
John Annan
John Annan is a notable individual who shares the surname associated with prominent figures such as former UN Secretary-General Kofi Annan.
-
E.
Noel Annan
Noel Annan was a British historian, intelligence officer, and academic administrator known for his influential work on modern intellectual history and his leadership roles at University College London and King's College, Cambridge.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d70fcb1cd881909635def59ad5d19c |
completed | April 9, 2026, 2:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de84932abc8190907c32720e35442e |
completed | April 14, 2026, 6:16 p.m. |
Created at: April 8, 2026, 9:14 p.m.