Triple
T2208636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | muon neutrino |
E50860
|
entity |
| Predicate | hasChirality |
P3977
|
FINISHED |
| Object | left-handed (for active states) |
—
|
LITERAL 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: left-handed (for active states) | Statement: [muon neutrino, hasChirality, left-handed (for active states)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChirality Context triple: [muon neutrino, hasChirality, left-handed (for active states)]
-
A.
hasChargeConjugation
Indicates that one entity is the charge-conjugated counterpart (particle vs. antiparticle form) of another entity.
-
B.
hasHelicity
chosen
Indicates that an entity possesses a specific helicity, i.e., a handedness or twist-related property in its structure or motion.
-
C.
hasSkewness
Indicates that a distribution or dataset exhibits a specific degree and direction of asymmetry around its central value.
-
D.
hasChaoticRotation
Indicates that an object rotates in a highly irregular, unpredictable manner rather than following a stable, consistent spin.
-
E.
haveDualityProperty
Indicates that an entity possesses a characteristic or state that inherently consists of two complementary, contrasting, or coexisting aspects.
- F. None of above.
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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.