Triple
T13021237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hope van Dyne |
E326175
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Pym Technologies |
E702535
|
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: Pym Technologies | Statement: [Hope van Dyne, employer, Pym Technologies]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pym Technologies Context triple: [Hope van Dyne, employer, Pym Technologies]
-
A.
Pym Technologies
chosen
Pym Technologies is a fictional scientific research and technology company in the Marvel universe, founded by Hank Pym and known for its groundbreaking size-altering Pym Particles.
-
B.
Paillant
Paillant is a commune located within Haiti’s Nippes Department, known as one of the rural settlements in the southwestern part of the country.
-
C.
Pyra Labs
Pyra Labs is the software company best known for creating Blogger, one of the earliest and most influential web-based blogging platforms.
-
D.
Eiffel Software
Eiffel Software is a software company best known for developing the Eiffel programming language and tools that emphasize object-oriented design and software reliability.
-
E.
Palantir Technologies
Palantir Technologies is an American software company specializing in big data analytics platforms used by governments and large enterprises for intelligence, security, and operational decision-making.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecf21bc819082fb512bc479b4be |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c119e19c81908ae2b1caff6f2f32 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:52 p.m.