Triple

T3214563
Position Surface form Disambiguated ID Type / Status
Subject Mutual Broadcasting System E67358 entity
Predicate foundingMemberStation P394 FINISHED
Object WXYZ
WXYZ is a historic American radio station best known as one of the original affiliates of the Mutual Broadcasting System network.
 E337153 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: WXYZ | Statement: [Mutual Broadcasting System, foundingMemberStation, WXYZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WXYZ
Context triple: [Mutual Broadcasting System, foundingMemberStation, WXYZ]
  • A. ZW
    ZW is the ISO 3166-1 alpha-2 country code for Zimbabwe, a landlocked country in southern Africa.
  • B. WZ
    WZ is the IATA airline designator assigned to Red Wings Airlines, a Russian passenger carrier.
  • C. ZWN
    ZWN is a former currency code used to denote an early version of the Zimbabwean dollar in international financial and foreign exchange contexts.
  • D. ZA
    ZA is the ISO 3166-1 alpha-2 country code for South Africa.
  • E. WY
    WY is the New York Stock Exchange ticker symbol for Weyerhaeuser Company, one of the world’s largest private owners of timberlands and a major forest products company.
  • 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: WXYZ
Triple: [Mutual Broadcasting System, foundingMemberStation, WXYZ]
Generated description
WXYZ is a historic American radio station best known as one of the original affiliates of the Mutual Broadcasting System network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WXYZ
Target entity description: WXYZ is a historic American radio station best known as one of the original affiliates of the Mutual Broadcasting System network.
  • A. ZW
    ZW is the ISO 3166-1 alpha-2 country code for Zimbabwe, a landlocked country in southern Africa.
  • B. WZ
    WZ is the IATA airline designator assigned to Red Wings Airlines, a Russian passenger carrier.
  • C. ZWN
    ZWN is a former currency code used to denote an early version of the Zimbabwean dollar in international financial and foreign exchange contexts.
  • D. ZA
    ZA is the ISO 3166-1 alpha-2 country code for South Africa.
  • E. WY
    WY is the New York Stock Exchange ticker symbol for Weyerhaeuser Company, one of the world’s largest private owners of timberlands and a major forest products company.
  • 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_69ad858ac36c81909962589cd277d6e2 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaabd01d48190be0dc610b9987a25 completed March 8, 2026, 4:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2623c90cc819085a94adfe3eb3f3f completed March 12, 2026, 6:50 a.m.
NEDg Description generation batch_69b2630060288190b0cf236863a5bb69 completed March 12, 2026, 6:53 a.m.
NED2 Entity disambiguation (via description) batch_69b264cbe2a48190baad7f335cdc37ba completed March 12, 2026, 7:01 a.m.
Created at: March 8, 2026, 3:07 p.m.