Triple
T5877596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwinger model |
E130663
|
entity |
| Predicate | typicalFermionMass |
P67399
|
FINISHED |
| Object | zero (massless Schwinger model) |
—
|
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: zero (massless Schwinger model) | Statement: [Schwinger model, typicalFermionMass, zero (massless Schwinger model)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFermionMass Context triple: [Schwinger model, typicalFermionMass, zero (massless Schwinger model)]
-
A.
approximateMass
Indicates that one entity has a mass value that is an estimate or close approximation of the mass of another entity.
-
B.
hasMass_kg
Indicates that an entity possesses a specific mass measured in kilograms.
-
C.
typicalBodyType
Indicates that one entity is the usual or characteristic body type associated with another entity.
-
D.
typicalEnergyRange
Indicates the usual or characteristic range of energy values associated with an entity, process, or interaction.
-
E.
givesMassTo
Indicates that one entity transfers or assigns a certain amount of mass to another entity.
- F. None of above. chosen
Provenance (4 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0432fea5881909f5c291dd8db6105 |
completed | March 22, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69c033499ca08190bd26cee5b03f6306 |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c0432f06fc8190bc047d52ffc30d59 |
completed | March 22, 2026, 7:29 p.m. |
Created at: March 22, 2026, 3:57 p.m.