Triple
T1580446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gell-Mann–Nishijima formula |
E33750
|
entity |
| Predicate | includesQuantumNumber |
P23057
|
FINISHED |
| Object | baryon number B |
—
|
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: baryon number B | Statement: [Gell-Mann–Nishijima formula, includesQuantumNumber, baryon number B]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesQuantumNumber Context triple: [Gell-Mann–Nishijima formula, includesQuantumNumber, baryon number B]
-
A.
hasQuantumNumber
chosen
Indicates that an entity (such as a particle or state) is associated with a specific quantum number value characterizing one of its quantized properties.
-
B.
isQuantumOf
Indicates that one entity represents a discrete, indivisible unit or packet of the other entity in a quantized relationship.
-
C.
hasAtomicNumber
Indicates that one entity is the chemical element whose atomic number (number of protons in its nucleus) is given by the other entity.
-
D.
hasValenceQuarks
Indicates that one entity possesses the specified quarks as its constituent valence quarks.
-
E.
hasProtonNumber
Indicates that an entity (typically a chemical element or atom) possesses a specific number of protons in its nucleus.
- 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_69a885f27a4c8190a4622252cdf54c00 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abacfb1144819080c5687175aba1e1 |
completed | March 7, 2026, 4:43 a.m. |
| PD | Predicate disambiguation | batch_69aa61b0f5bc8190b1dc272990a59c13 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:27 p.m.