Triple
T13801961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TTWO |
E331662
|
entity |
| Predicate | issuerSubsidiaryBrand |
P64617
|
FINISHED |
| Object | Zynga |
E331665
|
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: Zynga | Statement: [TTWO, issuerSubsidiaryBrand, Zynga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zynga Context triple: [TTWO, issuerSubsidiaryBrand, Zynga]
-
A.
Zynga
chosen
Zynga is a video game developer and publisher best known for its popular social and mobile games such as FarmVille and Words With Friends.
-
B.
Skydance Interactive
Skydance Interactive is a video game development and virtual reality studio known for creating immersive, narrative-driven gaming experiences.
-
C.
Gameloft
Gameloft is a French video game developer and publisher best known for creating and distributing mobile games worldwide.
-
D.
PopCap Games
PopCap Games is a video game developer best known for creating popular casual titles such as Bejeweled and Plants vs. Zombies.
-
E.
Glu Mobile
Glu Mobile is an American mobile game developer and publisher known for creating popular free-to-play titles, including celebrity-branded games.
- 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de025ded6c8190851c108ae559a355 |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b0893a20819081d4001b8dbc9c36 |
completed | May 3, 2026, 8:31 p.m. |
Created at: April 9, 2026, 10:12 p.m.