Triple
T16544193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jane Skinner |
E401898
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
WITI-TV
WITI-TV is a Fox-affiliated television station serving the Milwaukee, Wisconsin media market.
|
E1220006
|
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: WITI-TV | Statement: [Jane Skinner, employer, WITI-TV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WITI-TV Context triple: [Jane Skinner, employer, WITI-TV]
-
A.
WIVB-TV
WIVB-TV is a television station serving the Buffalo, New York market, known for its local news and CBS-affiliated programming.
-
B.
WABI-TV
WABI-TV is a long-running CBS-affiliated television station serving the Bangor, Maine market with local news, weather, and entertainment programming.
-
C.
WNEW-TV
WNEW-TV is a New York City television station historically known as an independent outlet that later became part of the Fox network.
-
D.
WSAW-TV
WSAW-TV is a local television station serving the Wausau, Wisconsin area, providing news, weather, and entertainment programming to regional viewers.
-
E.
WISH-TV
WISH-TV is a long-running Indianapolis-based television station known for its local news coverage and for having employed prominent broadcasters such as Jane Pauley early in their careers.
- 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: WITI-TV Triple: [Jane Skinner, employer, WITI-TV]
Generated description
WITI-TV is a Fox-affiliated television station serving the Milwaukee, Wisconsin media market.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WITI-TV Target entity description: WITI-TV is a Fox-affiliated television station serving the Milwaukee, Wisconsin media market.
-
A.
WIVB-TV
WIVB-TV is a television station serving the Buffalo, New York market, known for its local news and CBS-affiliated programming.
-
B.
WABI-TV
WABI-TV is a long-running CBS-affiliated television station serving the Bangor, Maine market with local news, weather, and entertainment programming.
-
C.
WNEW-TV
WNEW-TV is a New York City television station historically known as an independent outlet that later became part of the Fox network.
-
D.
WSAW-TV
WSAW-TV is a local television station serving the Wausau, Wisconsin area, providing news, weather, and entertainment programming to regional viewers.
-
E.
WISH-TV
WISH-TV is a long-running Indianapolis-based television station known for its local news coverage and for having employed prominent broadcasters such as Jane Pauley early in their careers.
- 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e34560daf08190b353b415d8ab280d |
completed | April 18, 2026, 8:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0067b3248c8190b63793bfc072aa4f |
completed | May 10, 2026, 11:10 a.m. |
| NEDg | Description generation | batch_6a0069d3b1c4819093c99516843cace6 |
completed | May 10, 2026, 11:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a006a4febf0819090471a73e88fdaf2 |
completed | May 10, 2026, 11:21 a.m. |
Created at: April 10, 2026, 5:15 a.m.