Triple
T3149647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fox Television Stations |
E65844
|
entity |
| Predicate | ownsStation |
P19286
|
FINISHED |
| Object |
WAGA-TV
WAGA-TV is a Fox-affiliated television station serving the Atlanta, Georgia market.
|
E331300
|
NE FINISHED |
How this triple was built (4 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: WAGA-TV | Statement: [Fox Television Stations, ownsStation, WAGA-TV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WAGA-TV Context triple: [Fox Television Stations, ownsStation, WAGA-TV]
-
A.
WGN-TV
WGN-TV is a Chicago-based television station and former national superstation known for its local news, sports broadcasts, and syndicated programming.
-
B.
WSNS-TV
WSNS-TV is a Spanish-language television station in Chicago affiliated with Telemundo and owned by NBCUniversal.
-
C.
TV5 Network
TV5 Network is a major Philippine television and media company known for its free-to-air channel TV5 and various entertainment, news, and sports programming.
-
D.
GMA News TV
GMA News TV is a Philippine free-to-air television network known for its news, public affairs, and informational programming.
-
E.
WGN America
WGN America is a U.S.-based cable television network known for airing syndicated series, movies, and original programming to a national audience.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: WAGA-TV Triple: [Fox Television Stations, ownsStation, WAGA-TV]
Generated description
WAGA-TV is a Fox-affiliated television station serving the Atlanta, Georgia market.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WAGA-TV Target entity description: WAGA-TV is a Fox-affiliated television station serving the Atlanta, Georgia market.
-
A.
WGN-TV
WGN-TV is a Chicago-based television station and former national superstation known for its local news, sports broadcasts, and syndicated programming.
-
B.
WSNS-TV
WSNS-TV is a Spanish-language television station in Chicago affiliated with Telemundo and owned by NBCUniversal.
-
C.
TV5 Network
TV5 Network is a major Philippine television and media company known for its free-to-air channel TV5 and various entertainment, news, and sports programming.
-
D.
GMA News TV
GMA News TV is a Philippine free-to-air television network known for its news, public affairs, and informational programming.
-
E.
WGN America
WGN America is a U.S.-based cable television network known for airing syndicated series, movies, and original programming to a national audience.
- F. None of above. chosen
Provenance (5 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_69ad8584485081909ed529e890cadc4a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5bf902c8190a490fa55e2dcecc0 |
completed | March 8, 2026, 4:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b224f94f3881909a277c45c9add0f5 |
completed | March 12, 2026, 2:29 a.m. |
| NEDg | Description generation | batch_69b225b27e1c8190a3df0d4692ee66c6 |
completed | March 12, 2026, 2:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b226339e4881908690f7ea7a7bd50c |
completed | March 12, 2026, 2:34 a.m. |
Created at: March 8, 2026, 3:05 p.m.