Triple
T11919094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fitting ideal |
E283606
|
entity |
| Predicate | inModuleDecomposition |
P73433
|
FINISHED |
| Object | helps distinguish nonisomorphic modules |
—
|
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: helps distinguish nonisomorphic modules | Statement: [Fitting ideal, inModuleDecomposition, helps distinguish nonisomorphic modules]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inModuleDecomposition Context triple: [Fitting ideal, inModuleDecomposition, helps distinguish nonisomorphic modules]
-
A.
decomposesIn
Indicates that one entity breaks down or separates into another entity or set of entities as its components or products.
-
B.
hasDecomposition
chosen
Indicates that something can be broken down or separated into constituent parts, components, or simpler elements.
-
C.
yieldsDecomposition
Indicates that one entity produces or results in a particular breakdown or decomposition of another entity.
-
D.
numericDecomposition
Indicates that a number is broken down into a set of component numbers or factors whose combination (e.g., sum or product) reconstructs the original value.
-
E.
decompositionType
Indicates the specific way in which a whole is broken down into its constituent parts or components.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e8dff77481908cacf6ad03df34ac |
completed | April 10, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3632ac8190b13e53c2b5db7125 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:44 p.m.