Triple

T10592667
Position Surface form Disambiguated ID Type / Status
Subject Aunty Entity E250029 entity
Predicate conflictsWith P4897 FINISHED
Object Max Rockatansky E195751 NE FINISHED

How this triple was built (2 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: Max Rockatansky | Statement: [Aunty Entity, conflictsWith, Max Rockatansky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Max Rockatansky
Context triple: [Aunty Entity, conflictsWith, Max Rockatansky]
  • A. Max Rockatansky chosen
    Max Rockatansky is the stoic, battle-hardened drifter and former cop who serves as the central antihero of the post-apocalyptic Mad Max film series.
  • B. Barrett Doss
    Barrett Doss is an American actress and singer best known for her starring role as firefighter Victoria Hughes on the television drama series "Station 19."
  • C. Doug Drummond
    Doug Drummond is a Canadian municipal politician who served as mayor of the city of Burnaby, British Columbia.
  • D. Ned Rawlins
    Ned Rawlins is a character in Robert Silverberg’s science fiction novel "The Man in the Maze," involved in the story’s exploration of alien contact and human isolation.
  • E. Korben Dallas
    Korben Dallas is the gruff yet heroic former special forces major-turned-taxi driver who becomes humanity’s reluctant savior in the sci-fi film "The Fifth Element."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d381c9d3d48190a29ee491e1696a0e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d5277da8048190add007ca0c37253e completed April 7, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69d95e85c49c8190a580536be07c0405 completed April 10, 2026, 8:33 p.m.
Created at: April 6, 2026, 12:40 p.m.