Triple
T5877580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwinger model |
E130663
|
entity |
| Predicate | hasAnomaly |
P67395
|
FINISHED |
| Object | chiral anomaly |
—
|
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: chiral anomaly | Statement: [Schwinger model, hasAnomaly, chiral anomaly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAnomaly Context triple: [Schwinger model, hasAnomaly, chiral anomaly]
-
A.
hasAberrationCharacteristics
Indicates that an entity exhibits traits or properties that deviate from what is considered normal, standard, or expected.
-
B.
hasException
Indicates that a general rule, process, or condition does not apply in a particular case due to a specified exception.
-
C.
hasIncidence
Indicates that a particular event, condition, or phenomenon occurs at a certain rate, frequency, or number of cases within a defined population or context.
-
D.
hasVariance
Indicates that there is a measurable degree of variability or dispersion in the values or outcomes associated with the related entities.
-
E.
hasNumberOfOverlooks
Indicates the specific count of overlooks (such as viewing points or vantage spots) associated with an 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.