Triple

T3875792
Position Surface form Disambiguated ID Type / Status
Subject Mars Science Laboratory E92496 entity
Predicate scienceInstruments P13781 FINISHED
Object DAN E86323 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: DAN | Statement: [Mars Science Laboratory, scienceInstruments, DAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DAN
Context triple: [Mars Science Laboratory, scienceInstruments, DAN]
  • A. DAN chosen
    DAN is a neutron-detecting scientific instrument on NASA's Curiosity rover used to measure subsurface hydrogen and infer the presence of water on Mars.
  • B. Dan
    Dan is the protagonist of Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," a post-scarcity future resident of a reputation-based society centered around a Disney theme park.
  • C. Dan
    Dan is a male given name commonly used in English-speaking countries, often as a short form of Daniel.
  • D. Dan
    Dan is a biblical figure recognized as one of the twelve sons of Jacob and the traditional ancestor of the Tribe of Dan in the Hebrew Bible.
  • E. Den
    Den was a prominent pharaoh of Egypt’s First Dynasty, known for early administrative innovations and military campaigns that helped consolidate the young Egyptian state.
  • 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_69aed967448c819086c4b358d37b25aa completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec706434819095e0d2b376adb548 completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5124f095881909143b624128ff569 completed March 14, 2026, 7:46 a.m.
Created at: March 9, 2026, 3:20 p.m.