Triple
T12655511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wanda Sykes |
E302270
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Wanda |
E253732
|
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: Wanda | Statement: [Wanda Sykes, givenName, Wanda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wanda Context triple: [Wanda Sykes, givenName, Wanda]
-
A.
Wanda
chosen
Wanda is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
-
B.
Wanda
Wanda is a fairy godparent character from the animated series "The Fairly OddParents," known for her responsible and level-headed personality.
-
C.
Wanda III
Wanda III is a historic Muskoka steam yacht, now preserved and operated as a heritage tour vessel in Ontario, Canada.
-
D.
Marvella
Marvella was an American political figure and advocate best known as the first wife of U.S. Senator Birch Bayh and for her work in Democratic politics and cancer awareness.
-
E.
Scarlet Witch
Scarlet Witch is a powerful Marvel Comics superhero and Avenger, known for her reality-warping chaos magic and complex moral journey.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961620b188190a8a8569f1133a9cf |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f668832c7081909eb75429efba493e |
completed | May 2, 2026, 9:11 p.m. |
Created at: April 9, 2026, 5:18 p.m.