Triple

T23269548
Position Surface form Disambiguated ID Type / Status
Subject XENON E588249 entity
Predicate hasComponent P35 FINISHED
Object XENONnT NE NERFINISHED

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: XENONnT | Statement: [XENON, hasComponent, XENONnT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: XENONnT
Context triple: [XENON, hasComponent, XENONnT]
  • A. Borexino
    Borexino is a large liquid scintillator neutrino detector located at Italy’s Gran Sasso National Laboratory, designed to study low-energy solar and other neutrinos with extremely low background levels.
  • B. XENON chosen
    XENON is a series of underground dark matter detection experiments using liquid xenon time projection chambers to search for weakly interacting massive particles (WIMPs).
  • C. CUORE
    CUORE is a cryogenic underground experiment designed to search for neutrinoless double-beta decay and study the fundamental properties of neutrinos.
  • D. MicroBooNE
    MicroBooNE is a liquid argon time projection chamber neutrino experiment at Fermilab designed to investigate the MiniBooNE low-energy excess and study neutrino interactions with high precision.
  • E. COSINE-100
    COSINE-100 is a dark matter direct-detection experiment using sodium iodide detectors, designed to independently test and verify the annual modulation signal reported by DAMA/LIBRA.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e25d148adc819088efbf42672604e9 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1957219188190b30bceffad1542da completed April 29, 2026, 5:21 a.m.
Created at: April 17, 2026, 4:45 p.m.