Triple

T5348026
Position Surface form Disambiguated ID Type / Status
Subject Take the "A" Train E124103 entity
Predicate recordLabel P1500 FINISHED
Object Victor
Victor was a prominent early 20th-century record label known for producing and distributing influential jazz and popular music recordings.
 E513287 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: Victor | Statement: [Take the "A" Train, recordLabel, Victor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Victor
Context triple: [Take the "A" Train, recordLabel, Victor]
  • A. Victor
    Victor is a masculine given name of Latin origin meaning "conqueror" or "winner," commonly used in many European and English-speaking countries.
  • B. Victor
    Victor is a central character in the TV series "Dollhouse," known as one of the programmable "Actives" whose identity and memories are repeatedly altered for various missions.
  • C. Víctor
    Víctor is a given name commonly used in Spanish-speaking countries, derived from the Latin name Victor meaning "winner" or "conqueror."
  • D. Viktor
    Viktor is the given name of Viktor Frankl, the Austrian neurologist, psychiatrist, and Holocaust survivor who founded logotherapy and wrote "Man’s Search for Meaning."
  • E. Viktor
    Viktor is a powerful and ancient vampire elder from the "Underworld" film series, portrayed by actor Bill Nighy.
  • 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: Victor
Triple: [Take the "A" Train, recordLabel, Victor]
Generated description
Victor was a prominent early 20th-century record label known for producing and distributing influential jazz and popular music recordings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Victor
Target entity description: Victor was a prominent early 20th-century record label known for producing and distributing influential jazz and popular music recordings.
  • A. Victor
    Victor is a masculine given name of Latin origin meaning "conqueror" or "winner," commonly used in many European and English-speaking countries.
  • B. Victor
    Victor is a central character in the TV series "Dollhouse," known as one of the programmable "Actives" whose identity and memories are repeatedly altered for various missions.
  • C. Víctor
    Víctor is a given name commonly used in Spanish-speaking countries, derived from the Latin name Victor meaning "winner" or "conqueror."
  • D. Viktor
    Viktor is the given name of Viktor Frankl, the Austrian neurologist, psychiatrist, and Holocaust survivor who founded logotherapy and wrote "Man’s Search for Meaning."
  • E. Viktor
    Viktor is a powerful and ancient vampire elder from the "Underworld" film series, portrayed by actor Bill Nighy.
  • 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_69bd464be27081908807b40b75c1bbae completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd85ef75148190815461c2a49302e9 completed March 20, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf21cd34a08190857dd960c12fec0c completed March 21, 2026, 10:55 p.m.
NEDg Description generation batch_69bf227781bc819083b8aba59618cc46 completed March 21, 2026, 10:57 p.m.
NED2 Entity disambiguation (via description) batch_69bf231d41848190b67de46bdbb38ab3 completed March 21, 2026, 11 p.m.
Created at: March 20, 2026, 2:01 p.m.