Triple
T21005905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thor franchise |
E517412
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Jane Foster |
—
|
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: Jane Foster | Statement: [Thor franchise, featuresCharacter, Jane Foster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jane Foster Context triple: [Thor franchise, featuresCharacter, Jane Foster]
-
A.
Jane Foster
chosen
Jane Foster is a brilliant astrophysicist and Thor’s primary human love interest in Marvel’s Thor films.
-
B.
Gwen Tyler
Gwen Tyler is a fictional character featured in a toy line, likely designed as part of a themed set or narrative-driven collection.
-
C.
Jennifer Walters
Jennifer Walters is a Marvel Comics lawyer who becomes the superhero She-Hulk after receiving a blood transfusion from her cousin Bruce Banner.
-
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.
Titania (MCU)
Titania (MCU) is a super-strong social media influencer and recurring antagonist in the Marvel Cinematic Universe, primarily known for clashing with She-Hulk/Jennifer Walters.
- 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_69e0b50192308190a284fcc89dd23a49 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc3c05a481908d25de2a63a4cdbe |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:52 p.m.