Triple

T1375203
Position Surface form Disambiguated ID Type / Status
Subject Ted Turner E29205 entity
Predicate founded P104 FINISHED
Object TCM
TCM (Turner Classic Movies) is a cable television network known for broadcasting classic films, primarily from Hollywood’s golden age, commercial-free and with expert introductions.
 E158996 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: TCM | Statement: [Ted Turner, founded, TCM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TCM
Context triple: [Ted Turner, founded, TCM]
  • A. TCM
    TCM is the IATA airport code for McChord Field at Joint Base Lewis–McChord in Washington State, USA.
  • B. TC
    TC is the standard abbreviation for the IEEE Transactions on Computers, a leading peer-reviewed journal covering research in computer science and engineering.
  • C. TC
    TC is the common abbreviation for the Trilateral Commission, a non-governmental policy discussion group that brings together leaders from North America, Europe, and Asia to address global issues.
  • D. TC
    TC is the Constitutional Court of Peru, the country’s highest body responsible for interpreting and safeguarding the constitution and constitutional rights.
  • E. TC
    TC is the two-letter ISO 3166-1 alpha-2 country code assigned to the Turks and Caicos Islands.
  • 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: TCM
Triple: [Ted Turner, founded, TCM]
Generated description
TCM (Turner Classic Movies) is a cable television network known for broadcasting classic films, primarily from Hollywood’s golden age, commercial-free and with expert introductions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TCM
Target entity description: TCM (Turner Classic Movies) is a cable television network known for broadcasting classic films, primarily from Hollywood’s golden age, commercial-free and with expert introductions.
  • A. TCM
    TCM is the IATA airport code for McChord Field at Joint Base Lewis–McChord in Washington State, USA.
  • B. TC
    TC is the standard abbreviation for the IEEE Transactions on Computers, a leading peer-reviewed journal covering research in computer science and engineering.
  • C. TC
    TC is the common abbreviation for the Trilateral Commission, a non-governmental policy discussion group that brings together leaders from North America, Europe, and Asia to address global issues.
  • D. TC
    TC is the Constitutional Court of Peru, the country’s highest body responsible for interpreting and safeguarding the constitution and constitutional rights.
  • E. TC
    TC is the two-letter ISO 3166-1 alpha-2 country code assigned to the Turks and Caicos Islands.
  • 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_69a498d883a48190bfdca525296ef7ee completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c2f7aeb08190b52ef1058c18327e completed March 1, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69acd485e7e48190b4c2dcec53ebe195 completed March 8, 2026, 1:44 a.m.
NEDg Description generation batch_69acd971f3088190adc54e5eed0e3e0b completed March 8, 2026, 2:05 a.m.
NED2 Entity disambiguation (via description) batch_69acda1c9fac8190a825533837f612b2 completed March 8, 2026, 2:08 a.m.
Created at: March 1, 2026, 7:59 p.m.