Triple

T3928055
Position Surface form Disambiguated ID Type / Status
Subject BP E93325 entity
Predicate brand P1500 FINISHED
Object Castrol E295034 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: Castrol | Statement: [BP, brand, Castrol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Castrol
Context triple: [BP, brand, Castrol]
  • A. Castrol chosen
    Castrol is a global brand of industrial and automotive lubricants, best known for its high-performance motor oils and long-standing involvement in motorsports sponsorship.
  • B. S-Oil
    S-Oil is a major South Korean oil refining and petrochemical company known for operating large-scale refinery facilities and producing fuels and lubricants for domestic and international markets.
  • C. Lucas Oil
    Lucas Oil is an American manufacturer and distributor of automotive oils, lubricants, and additives widely used in consumer, commercial, and motorsports applications.
  • D. Lubrizol
    Lubrizol is a specialty chemicals company best known for producing additives and advanced materials used in lubricants, personal care products, and industrial applications.
  • E. Caltex
    Caltex is an international petroleum brand of Chevron Corporation, known for its network of fuel stations and lubricants across Asia-Pacific, Africa, and the Middle East.
  • 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_69aed96bfa1081908f7b30f2c647dee6 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeeda4f9d481908dda1b5a826ab64d completed March 9, 2026, 3:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5287b8d548190a929f14637cb9963 completed March 14, 2026, 9:20 a.m.
Created at: March 9, 2026, 3:23 p.m.