Triple

T17914409
Position Surface form Disambiguated ID Type / Status
Subject Renault 4 E447888 entity
Predicate alsoKnownAs P39 FINISHED
Object R4
R4 is the common shorthand for the Renault 4, a popular small economy car produced by the French manufacturer Renault from the early 1960s through the early 1990s.
 E1296185 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: R4 | Statement: [Renault 4, alsoKnownAs, R4]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R4
Context triple: [Renault 4, alsoKnownAs, R4]
  • A. R4
    R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • B. R4
    R4 is a commuter rail line in the Rodalies de Catalunya network serving key suburban and regional routes in Catalonia, Spain.
  • C. R-4
    The R-4 is a World War II–era Sikorsky helicopter recognized as the first mass-produced helicopter and the first to be used operationally by the U.S. military.
  • D. 4R
    4R is a designated runway number used at airports, indicating a right-hand runway aligned roughly with a 040-degree magnetic heading.
  • E. R40
    The R40 was a class of New York City Subway cars built in the late 1960s, notable for their futuristic slanted-end design and service on B Division lines.
  • 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: R4
Triple: [Renault 4, alsoKnownAs, R4]
Generated description
R4 is the common shorthand for the Renault 4, a popular small economy car produced by the French manufacturer Renault from the early 1960s through the early 1990s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R4
Target entity description: R4 is the common shorthand for the Renault 4, a popular small economy car produced by the French manufacturer Renault from the early 1960s through the early 1990s.
  • A. R4
    R4 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • B. R4
    R4 is a commuter rail line in the Rodalies de Catalunya network serving key suburban and regional routes in Catalonia, Spain.
  • C. R-4
    The R-4 is a World War II–era Sikorsky helicopter recognized as the first mass-produced helicopter and the first to be used operationally by the U.S. military.
  • D. 4R
    4R is a designated runway number used at airports, indicating a right-hand runway aligned roughly with a 040-degree magnetic heading.
  • E. R40
    The R40 was a class of New York City Subway cars built in the late 1960s, notable for their futuristic slanted-end design and service on B Division lines.
  • 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_69d8b9f6d394819082a6d69fd1e23d2f completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4a30461848190b932a45807329216 completed April 19, 2026, 9:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a03212ab44c81909120d15c283d1c3c completed May 12, 2026, 12:46 p.m.
NEDg Description generation batch_6a03220af8dc8190aec1469d5c3d63be completed May 12, 2026, 12:50 p.m.
NED2 Entity disambiguation (via description) batch_6a032277e9c8819084a9d4c6f559bebc completed May 12, 2026, 12:52 p.m.
Created at: April 10, 2026, 10:20 a.m.