Triple
T26765429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | up quark |
E674924
|
entity |
| Predicate | quarkContentExample |
P78100
|
FINISHED |
| Object | proton has two up quarks and one down quark |
—
|
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: proton has two up quarks and one down quark | Statement: [up quark, quarkContentExample, proton has two up quarks and one down quark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: quarkContentExample Context triple: [up quark, quarkContentExample, proton has two up quarks and one down quark]
-
A.
quarkContent
chosen
Indicates the specific types and numbers of quarks that make up a given particle.
-
B.
quarkModel
Indicates that one entity represents or employs a quark-based theoretical model to describe the internal structure or behavior of another entity (such as a particle or system).
-
C.
GCContent
Indicates the proportion of guanine (G) and cytosine (C) bases relative to the total nucleotide content in a DNA or RNA sequence.
-
D.
collectsContent
Indicates that one entity gathers, acquires, or accumulates content from another entity or source.
-
E.
baryonExamplesContaining
Indicates that certain example instances are composed of or include one or more baryons.
- 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_69eecda85298819097ee1c38a3d772e7 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6192758648190ba2c0bfc9904994e |
completed | May 2, 2026, 3:32 p.m. |
| PD | Predicate disambiguation | batch_69f611ad2eb48190ac1ed0090f13f7a9 |
completed | May 2, 2026, 3:01 p.m. |
Created at: April 27, 2026, 3:59 a.m.