Triple

T20651068
Position Surface form Disambiguated ID Type / Status
Subject The Phil Donahue Show E507493 entity
Predicate originalNetwork P2594 FINISHED
Object WLWD
WLWD was a Dayton, Ohio television station that served as the original broadcast home of The Phil Donahue Show and later became known as WDTN.
 E1442443 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: WLWD | Statement: [The Phil Donahue Show, originalNetwork, WLWD]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WLWD
Context triple: [The Phil Donahue Show, originalNetwork, WLWD]
  • A. WLV
    WLV is the National Rail station code for Wallasey Village railway station on the Wirral Line in Merseyside, England.
  • B. WDOL
    WDOL (Wage Determinations OnLine) was a U.S. government website that provided federal contracting wage determinations before its functions were consolidated into SAM.gov.
  • C. WLND
    WLND is the station code for Wonderland, a public transit station.
  • D. WLN
    WLN is the stock ticker symbol for Worldline, a major European provider of payment and transactional services.
  • E. WNLO
    WNLO is a major Chinese research institute specializing in optoelectronics and photonics, based in Wuhan.
  • 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: WLWD
Triple: [The Phil Donahue Show, originalNetwork, WLWD]
Generated description
WLWD was a Dayton, Ohio television station that served as the original broadcast home of The Phil Donahue Show and later became known as WDTN.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WLWD
Target entity description: WLWD was a Dayton, Ohio television station that served as the original broadcast home of The Phil Donahue Show and later became known as WDTN.
  • A. WLV
    WLV is the National Rail station code for Wallasey Village railway station on the Wirral Line in Merseyside, England.
  • B. WDOL
    WDOL (Wage Determinations OnLine) was a U.S. government website that provided federal contracting wage determinations before its functions were consolidated into SAM.gov.
  • C. WLND
    WLND is the station code for Wonderland, a public transit station.
  • D. WLN
    WLN is the stock ticker symbol for Worldline, a major European provider of payment and transactional services.
  • E. WNLO
    WNLO is a major Chinese research institute specializing in optoelectronics and photonics, based in Wuhan.
  • 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_69e0b4bf58c081908e52a4500e03ff83 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6af21e87c8190835bf2b2ef195626 completed April 20, 2026, 10:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a08c59354388190b5c2a0c1bdd14575 completed May 16, 2026, 7:29 p.m.
NEDg Description generation batch_6a08c648e35881908c480b0afa383422 completed May 16, 2026, 7:32 p.m.
NED2 Entity disambiguation (via description) batch_6a08c6c6f9608190bdbcdd58f6b14008 completed May 16, 2026, 7:34 p.m.
Created at: April 16, 2026, 11:43 a.m.