Triple

T2132974
Position Surface form Disambiguated ID Type / Status
Subject MLS Cup 2021 E46583 entity
Predicate broadcastNetworkUS P833 FINISHED
Object TUDN
TUDN is a Spanish-language sports television network and media brand focused on soccer and other sports, primarily serving audiences in the United States and Mexico.
 E237952 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: TUDN | Statement: [MLS Cup 2021, broadcastNetworkUS, TUDN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TUDN
Context triple: [MLS Cup 2021, broadcastNetworkUS, TUDN]
  • A. TNUA
    TNUA is an academic association or network that includes Nagoya University among its member institutions.
  • B. T.D.
    T.D. is the anthropomorphic dolphin mascot who entertains fans and represents the Miami Dolphins NFL team at games and events.
  • C. ZTU
    ZTU is the IATA station code assigned to a specific passenger rail station in Miami, Florida, used for ticketing and travel logistics.
  • D. Tus
    Tus is an ancient city in northeastern Iran, renowned as a cultural and literary center and traditionally regarded as the birthplace and home of the Persian epic poet Ferdowsi.
  • E. TU9
    TU9 is an alliance of nine leading German Institutes of Technology focused on engineering and natural sciences research and education.
  • 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: TUDN
Triple: [MLS Cup 2021, broadcastNetworkUS, TUDN]
Generated description
TUDN is a Spanish-language sports television network and media brand focused on soccer and other sports, primarily serving audiences in the United States and Mexico.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TUDN
Target entity description: TUDN is a Spanish-language sports television network and media brand focused on soccer and other sports, primarily serving audiences in the United States and Mexico.
  • A. TNUA
    TNUA is an academic association or network that includes Nagoya University among its member institutions.
  • B. T.D.
    T.D. is the anthropomorphic dolphin mascot who entertains fans and represents the Miami Dolphins NFL team at games and events.
  • C. ZTU
    ZTU is the IATA station code assigned to a specific passenger rail station in Miami, Florida, used for ticketing and travel logistics.
  • D. Tus
    Tus is an ancient city in northeastern Iran, renowned as a cultural and literary center and traditionally regarded as the birthplace and home of the Persian epic poet Ferdowsi.
  • E. TU9
    TU9 is an alliance of nine leading German Institutes of Technology focused on engineering and natural sciences research and education.
  • 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_69a88a1626548190ae59a5028c3baa8e completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbba0c42c8190ab3ce4bbf1531ee1 completed March 7, 2026, 5:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae51a82a7c8190bc6737034d01f176 completed March 9, 2026, 4:50 a.m.
NEDg Description generation batch_69ae528634608190bf10e3abf5a2c2d9 completed March 9, 2026, 4:54 a.m.
NED2 Entity disambiguation (via description) batch_69ae536431bc8190b9f293d74046cb27 completed March 9, 2026, 4:58 a.m.
Created at: March 4, 2026, 7:44 p.m.