Triple

T15248948
Position Surface form Disambiguated ID Type / Status
Subject Mercy E364464 entity
Predicate hasShortFormOf P8075 FINISHED
Object Mercedes (in some contexts) E1141049 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: Mercedes (in some contexts) | Statement: [Mercy, hasShortFormOf, Mercedes (in some contexts)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mercedes (in some contexts)
Context triple: [Mercy, hasShortFormOf, Mercedes (in some contexts)]
  • A. Mercedes (automobile brand) chosen
    Mercedes is a renowned German luxury automobile brand known for its high-quality engineering, innovation, and premium vehicles.
  • B. Mercedes
    Mercedes is a courageous and compassionate housekeeper who secretly aids the Spanish Maquis resistance in Guillermo del Toro’s dark fantasy film "Pan’s Labyrinth."
  • C. Mercedes
    Mercedes is a coastal municipality in the Philippine province of Camarines Norte known for its fishing industry and nearby island attractions.
  • D. Mercedes
    Mercedes is the given first name of the British former ballerina and television personality Darcey Bussell.
  • E. Mercedes
    Mercedes is a German Formula One team and automotive manufacturer renowned for its dominant performance in the early hybrid era of F1.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f62b9c8190b9ad40e2d1912b63 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd4ac4e48190b011b34cb5205b68 completed May 9, 2026, 7:07 a.m.
Created at: April 10, 2026, 3:13 a.m.