Triple
T16570453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yolanda of Hungary |
E402569
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Yolanda |
E47296
|
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: Yolanda | Statement: [Yolanda of Hungary, givenName, Yolanda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yolanda Context triple: [Yolanda of Hungary, givenName, Yolanda]
-
A.
Yolanda
chosen
Yolanda is a feminine given name used in various cultures, often associated with figures in the arts, activism, and public life.
-
B.
Wilma
Wilma was a catastrophic 2005 Atlantic hurricane that became one of the most intense on record, causing widespread destruction in the Caribbean and the United States.
-
C.
Irma
Irma is a feminine given name used in various European and Latin American cultures, often considered a variant or related form of names like Emma or Irmina.
-
D.
Yolandi
Yolandi is a central character in the science-fiction film "Chappie," portrayed by South African musician and actress Yolandi Visser.
-
E.
Debby
Debby is a feminine given name commonly used as a diminutive form of Deborah or Debra.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e359580f548190a21afd148bc22d8d |
completed | April 18, 2026, 10:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006ee8812c81908ef74636bf39d44a |
completed | May 10, 2026, 11:41 a.m. |
Created at: April 10, 2026, 5:16 a.m.