Triple
T18861928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CBS Interactive |
E461331
|
entity |
| Predicate | operated |
P1688
|
FINISHED |
| Object | GameSpot |
—
|
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: GameSpot | Statement: [CBS Interactive, operated, GameSpot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GameSpot Context triple: [CBS Interactive, operated, GameSpot]
-
A.
GameSpot
chosen
GameSpot is a popular video game journalism website known for its reviews, news, and industry awards.
-
B.
Kotaku
Kotaku is a popular video game and entertainment website known for its news, reviews, and commentary on gaming culture.
-
C.
Eurogamer
Eurogamer is a prominent video game journalism website known for its news, reviews, and in-depth coverage of the gaming industry.
-
D.
PC Gamer
PC Gamer is a leading video game magazine and website focused on news, reviews, and features about PC gaming hardware and software.
-
E.
GamePro
GamePro was a popular multi-platform video game magazine and media brand known for its reviews, previews, and gaming industry coverage, especially during the 1990s and early 2000s.
- 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c06184d88190bd05413a07a8c9ce |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 10, 2026, 11:57 a.m.