Triple
T33588377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | electron antineutrino |
E860351
|
entity |
| Predicate | isNearlyMassless |
P131625
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [electron antineutrino, isNearlyMassless, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isNearlyMassless Context triple: [electron antineutrino, isNearlyMassless, true]
-
A.
isMasslessIn
Indicates that an entity has zero rest mass within a specified context, system, or environment.
-
B.
approximateMass
Indicates that one entity has a mass value that is an estimate or close approximation of the mass of another entity.
-
C.
hasLowMass
chosen
Indicates that the subject possesses a relatively small amount of mass compared to a given standard or reference.
-
D.
hasMassScale
Indicates that an entity is associated with a particular mass measurement scale or system used to quantify its mass.
-
E.
isVerySmallNEA
Indicates that an object is classified as a very small near-Earth asteroid relative to typical NEAs.
- 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_69f3497e70e48190951c94d072879bec |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f77626d08190821cdc5a96e621fb |
completed | May 3, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:40 a.m.