Triple
T1770975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wonder Woman (2017 film) |
E38872
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Connie Nielsen |
E153176
|
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: Connie Nielsen | Statement: [Wonder Woman (2017 film), castMember, Connie Nielsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Connie Nielsen Context triple: [Wonder Woman (2017 film), castMember, Connie Nielsen]
-
A.
Connie Nielsen
chosen
Connie Nielsen is a Danish actress best known for her roles in films such as "Gladiator" and "Wonder Woman."
-
B.
Jaimie Alexander
Jaimie Alexander is an American actress best known for her role as the Asgardian warrior Lady Sif in the Marvel Cinematic Universe and for starring in the TV series "Blindspot."
-
C.
Anna Watson
Anna Watson is a fictional character known primarily as the rival of Rachel Watson.
-
D.
Kate Beckinsale
Kate Beckinsale is an English actress known for her versatile film career, including prominent roles in action, drama, and comedy films such as the Underworld series and various Hollywood productions.
-
E.
Kristin Scott Thomas
Kristin Scott Thomas is an acclaimed British actress known for her nuanced performances in films such as "The English Patient," "Four Weddings and a Funeral," and "The Horse Whisperer."
- 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa648fe908819098fd27b74b17fabb |
completed | March 6, 2026, 5:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada9936f5881909b78bda48039916a |
completed | March 8, 2026, 4:53 p.m. |
Created at: March 4, 2026, 7:31 p.m.