Triple
T419270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Treatise on Money |
E8063
|
entity |
| Predicate | hasSubjectCategory |
P10719
|
FINISHED |
| Object | monetary theory |
—
|
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: monetary theory | Statement: [A Treatise on Money, hasSubjectCategory, monetary theory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectCategory Context triple: [A Treatise on Money, hasSubjectCategory, monetary theory]
-
A.
hasMajorCategory
Indicates that something is associated with or classified under a primary, overarching category.
-
B.
hasNotableSubject
Indicates that an entity is associated with a subject that is particularly significant, prominent, or noteworthy in relation to it.
-
C.
hasBibliographicCategory
chosen
Indicates that an entity is associated with a specific bibliographic classification or category within a cataloging or documentation system.
-
D.
hasSubdiscipline
Indicates that one discipline includes another, more specialized field of study as a subordinate branch.
-
E.
subjectCanBe
Indicates that the subject has the potential or capability to assume, become, or be classified as the specified object or state.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd3b948819097d96c73d0a0f699 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.