Triple
T5598795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | J. Murrey Atkins Library |
E147062
|
entity |
| Predicate | hasSubjectAreaCoverage |
P934
|
FINISHED |
| Object | humanities |
—
|
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: humanities | Statement: [J. Murrey Atkins Library, hasSubjectAreaCoverage, humanities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectAreaCoverage Context triple: [J. Murrey Atkins Library, hasSubjectAreaCoverage, humanities]
-
A.
hasFrequencyCoverage
Indicates that one entity provides, supports, or is applicable across a specified range or set of frequencies associated with another entity.
-
B.
hasCoverageFocus
Indicates that one entity’s coverage, attention, or analysis is specifically focused on or directed toward another entity.
-
C.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
D.
hasResearchArea
chosen
Indicates that an entity (such as a person, project, or organization) is associated with or focused on a particular field or area of research.
-
E.
providesCoverage
Indicates that one entity supplies protection, insurance, or service coverage to another entity or for a specified risk or scope.
- 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_69c009043d648190a7af89698ccf1e3e |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020d82870819087f9591b5a1021ce |
completed | March 22, 2026, 5:03 p.m. |
| PD | Predicate disambiguation | batch_69c01b1890ec8190b9e6fa488792e4d4 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:38 p.m.