Triple
T3149649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fox Television Stations |
E65844
|
entity |
| Predicate | ownsStation |
P19286
|
FINISHED |
| Object |
KMSP-TV
KMSP-TV is a Fox-affiliated television station serving the Minneapolis–Saint Paul metropolitan area.
|
E331302
|
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: KMSP-TV | Statement: [Fox Television Stations, ownsStation, KMSP-TV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KMSP-TV Context triple: [Fox Television Stations, ownsStation, KMSP-TV]
-
A.
KING-TV
KING-TV is a Seattle-based NBC-affiliated television station known for its local news coverage and for originating popular educational programs such as "Bill Nye the Science Guy."
-
B.
KENW-TV
KENW-TV is a public television station in eastern New Mexico that serves as a regional PBS affiliate and broadcast outlet for Eastern New Mexico University.
-
C.
KTTV
KTTV is a Los Angeles-based television station, historically known as a major outlet for sports broadcasting and local news coverage.
-
D.
KTRK-TV
KTRK-TV is a Houston-based television station serving as the local ABC network affiliate.
-
E.
KRLD-TV
KRLD-TV is a Dallas–Fort Worth television station historically known for its local news coverage and role in reporting major events such as the assassination of President John F. Kennedy.
- 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: KMSP-TV Triple: [Fox Television Stations, ownsStation, KMSP-TV]
Generated description
KMSP-TV is a Fox-affiliated television station serving the Minneapolis–Saint Paul metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KMSP-TV Target entity description: KMSP-TV is a Fox-affiliated television station serving the Minneapolis–Saint Paul metropolitan area.
-
A.
KING-TV
KING-TV is a Seattle-based NBC-affiliated television station known for its local news coverage and for originating popular educational programs such as "Bill Nye the Science Guy."
-
B.
KENW-TV
KENW-TV is a public television station in eastern New Mexico that serves as a regional PBS affiliate and broadcast outlet for Eastern New Mexico University.
-
C.
KTTV
KTTV is a Los Angeles-based television station, historically known as a major outlet for sports broadcasting and local news coverage.
-
D.
KTRK-TV
KTRK-TV is a Houston-based television station serving as the local ABC network affiliate.
-
E.
KRLD-TV
KRLD-TV is a Dallas–Fort Worth television station historically known for its local news coverage and role in reporting major events such as the assassination of President John F. Kennedy.
- 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.