Triple
T13712658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janet van Dyne |
E328811
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Vision |
E123985
|
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: Vision | Statement: [Janet van Dyne, associatedWith, Vision]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vision Context triple: [Janet van Dyne, associatedWith, Vision]
-
A.
Vision
chosen
Vision is a powerful, synthetically created superhero in the Marvel universe known for his android body, advanced intellect, and moral complexity.
-
B.
Vision
Vision is Apple’s mixed-reality product line centered on advanced spatial computing headsets like the Apple Vision Pro.
-
C.
VIS
VIS is the IATA airport code for Visalia Municipal Airport in Visalia, California, United States.
-
D.
VIS
VIS is a large-scale European Union database system used to store and exchange visa application and related biometric data among member states’ authorities.
-
E.
See
See is a post-apocalyptic science fiction television series in which a future human society has lost the sense of sight, leading to unique cultural and power struggles.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d54a68081908df25edf6d5df362 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:54 p.m.