Triple

T2125020
Position Surface form Disambiguated ID Type / Status
Subject RenderMan E46404 entity
Predicate abbreviation P43 FINISHED
Object RSL
RSL is the shading language used in Pixar's RenderMan system to define the appearance of surfaces, lights, and volumes in high-end computer graphics rendering.
E236163 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: RSL | Statement: [RenderMan, abbreviation, RSL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RSL
Context triple: [RenderMan, abbreviation, RSL]
  • A. RSL
    RSL is the commonly used acronym for the Russian Superleague, a former top-tier professional ice hockey league in Russia.
  • B. RSJ
    RSJ is a professional society in Japan dedicated to advancing research, development, and dissemination of knowledge in the field of robotics.
  • C. RL
    RL is the commonly used acronym for the U.S. Department of Energy’s Richland Operations Office, which oversees environmental cleanup and related activities at the Hanford Site in Washington State.
  • D. RSM
    RSM is the international vehicle registration code used on license plates for vehicles registered in San Marino.
  • E. RSM
    RSM is the acronym for the NATO-led Resolute Support Mission in Afghanistan, focused on training, advising, and assisting Afghan security forces after the end of NATO’s combat operations.
  • 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: RSL
Triple: [RenderMan, abbreviation, RSL]
Generated description
RSL is the shading language used in Pixar's RenderMan system to define the appearance of surfaces, lights, and volumes in high-end computer graphics rendering.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: RSL
Target entity description: RSL is the shading language used in Pixar's RenderMan system to define the appearance of surfaces, lights, and volumes in high-end computer graphics rendering.
  • A. RSL
    RSL is the commonly used acronym for the Russian Superleague, a former top-tier professional ice hockey league in Russia.
  • B. RSJ
    RSJ is a professional society in Japan dedicated to advancing research, development, and dissemination of knowledge in the field of robotics.
  • C. RL
    RL is the commonly used acronym for the U.S. Department of Energy’s Richland Operations Office, which oversees environmental cleanup and related activities at the Hanford Site in Washington State.
  • D. RSM
    RSM is the international vehicle registration code used on license plates for vehicles registered in San Marino.
  • E. RSM
    RSM is the abbreviation commonly used for the Royal Schools of Music, a group of prestigious UK conservatoires known for their music education and examination programs.
  • 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_69abbb57bc6881909f04a407beff33a6 completed March 7, 2026, 5:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae519ead088190a8a7229d998732de completed March 9, 2026, 4:50 a.m.
NEDg Description generation batch_69ae526b6bcc8190851b9775611bb93e completed March 9, 2026, 4:54 a.m.
NED2 Entity disambiguation (via description) batch_69ae52d46ef48190bbd6b7cc9fcda2a4 completed March 9, 2026, 4:55 a.m.
Created at: March 4, 2026, 7:44 p.m.