Triple
T10880512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Snap |
E256906
|
entity |
| Predicate | opposedByCharacter |
P18963
|
FINISHED |
| Object | Scarlet Witch |
E199163
|
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: Scarlet Witch | Statement: [The Snap, opposedByCharacter, Scarlet Witch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scarlet Witch Context triple: [The Snap, opposedByCharacter, Scarlet Witch]
-
A.
Scarlet Witch
chosen
Scarlet Witch is a powerful Marvel Comics superhero and Avenger, known for her reality-warping chaos magic and complex moral journey.
-
B.
Luna Maximoff
Luna Maximoff is a Marvel Comics character, the daughter of Quicksilver and Crystal, notable as a human-Inhuman hybrid with empathic abilities.
-
C.
Agatha Harkness
Agatha Harkness is a powerful and cunning witch from Marvel Comics and the Marvel Cinematic Universe, known for manipulating magic and serving as both mentor and antagonist to other magic users like Wanda Maximoff.
-
D.
Monica Rambeau
Monica Rambeau is a Marvel Comics superhero and S.W.O.R.D. agent who gains energy-based powers and becomes a key figure in the Marvel Cinematic Universe.
-
E.
Wanda
Wanda is a feminine given name of Slavic origin, particularly common in Poland and other Central and Eastern European countries.
- 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_69d6aa848804819081b2713ca0bedf06 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d751b031a88190b1182dfc1f520264 |
completed | April 9, 2026, 7:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dff7e2322c8190a55605237ae6ce95 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 8, 2026, 9:21 p.m.