Triple
T23100475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wonder Woman |
E576008
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Gal Gadot |
—
|
NE NERFINISHED |
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: Gal Gadot | Statement: [Wonder Woman, portrayedBy, Gal Gadot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gal Gadot Context triple: [Wonder Woman, portrayedBy, Gal Gadot]
-
A.
Gal Gadot
chosen
Gal Gadot is an Israeli actress and model best known internationally for portraying Wonder Woman in the DC Extended Universe films.
-
B.
Ruby Rose
Ruby Rose is an Australian model, DJ, and actress known for her androgynous style and roles in action films and television series such as "Orange Is the New Black."
-
C.
Ana de Armas
Ana de Armas is a Cuban-Spanish actress known for her breakout roles in films such as "Blade Runner 2049," "Knives Out," and "Blonde."
-
D.
Margot Robbie
Margot Robbie is an Australian actress and producer known for her versatile performances in films such as "The Wolf of Wall Street," "I, Tonya," and "Barbie."
-
E.
Scarlett Johansson
Scarlett Johansson is an American actress known for her versatile film roles, including major parts in the Marvel Cinematic Universe and acclaimed performances in both independent and blockbuster movies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18de8c5b4819095cddf989cade60d |
completed | April 29, 2026, 4:49 a.m. |
Created at: April 17, 2026, 3:58 p.m.