Triple

T11550869
Position Surface form Disambiguated ID Type / Status
Subject Warnock algorithm E273884 entity
Predicate relatedTo P37 FINISHED
Object z-buffer algorithm
The z-buffer algorithm is a computer graphics technique for hidden surface determination that stores depth information for each pixel to correctly render overlapping objects in 3D scenes.
 E932699 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: z-buffer algorithm | Statement: [Warnock algorithm, relatedTo, z-buffer algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: z-buffer algorithm
Context triple: [Warnock algorithm, relatedTo, z-buffer algorithm]
  • A. Z3
    Z3 is a high-performance theorem prover and SMT (Satisfiability Modulo Theories) solver developed by Microsoft Research, widely used in formal verification, program analysis, and automated reasoning.
  • B. ZC
    ZC is the governing body responsible for overseeing and developing the sport of cricket in Zimbabwe.
  • C. ZP
    ZP is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • D. ZFF
    ZFF is the abbreviation for the Zurich Film Festival, an annual international film festival held in Zurich, Switzerland.
  • E. ZSP
    ZSP is the three-letter station code used to identify St James's Park tube station on the London Underground network.
  • 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: z-buffer algorithm
Triple: [Warnock algorithm, relatedTo, z-buffer algorithm]
Generated description
The z-buffer algorithm is a computer graphics technique for hidden surface determination that stores depth information for each pixel to correctly render overlapping objects in 3D scenes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: z-buffer algorithm
Target entity description: The z-buffer algorithm is a computer graphics technique for hidden surface determination that stores depth information for each pixel to correctly render overlapping objects in 3D scenes.
  • A. Z3
    Z3 is a high-performance theorem prover and SMT (Satisfiability Modulo Theories) solver developed by Microsoft Research, widely used in formal verification, program analysis, and automated reasoning.
  • B. ZC
    ZC is the governing body responsible for overseeing and developing the sport of cricket in Zimbabwe.
  • C. ZP
    ZP is a German vehicle registration code assigned to the Erzgebirgskreis district in the state of Saxony.
  • D. ZFF
    ZFF is the abbreviation for the Zurich Film Festival, an annual international film festival held in Zurich, Switzerland.
  • E. ZSP
    ZSP is the three-letter station code used to identify St James's Park tube station on the London Underground network.
  • 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_69d6aae4dfa48190a3ab0b19a159a3c5 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d88a83f1e88190aabf11a4c8a6c9e5 completed April 10, 2026, 5:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69e6e8396ed081909bdf381db3dacd62 completed April 21, 2026, 3 a.m.
NEDg Description generation batch_69e6ef8fca248190bc2fdd8457258874 completed April 21, 2026, 3:31 a.m.
NED2 Entity disambiguation (via description) batch_69e6f9144afc819081ee7f78e32ad39a completed April 21, 2026, 4:12 a.m.
Created at: April 8, 2026, 9:37 p.m.