Triple
T9547451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darcy Lewis |
E230329
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Jane Foster |
E197181
|
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: Jane Foster | Statement: [Darcy Lewis, employer, Jane Foster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jane Foster Context triple: [Darcy Lewis, employer, 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.
Sif
Sif is a goddess in Norse mythology best known as the golden-haired wife of Thor and a deity associated with earth, fertility, and grain.
- 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_69ca847c70b8819088a0a0bad64a50d6 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9904732c8190ab60ecc47c995cbe |
completed | April 1, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c7be5008190a16036525fd9059e |
completed | April 4, 2026, 5:38 p.m. |
Created at: March 30, 2026, 8:02 p.m.