Triple
T312747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Library of Congress Subject Headings |
E7641
|
entity |
| Predicate | hasApplicationArea |
P9752
|
FINISHED |
| Object | academic research |
—
|
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: academic research | Statement: [Library of Congress Subject Headings, hasApplicationArea, academic research]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApplicationArea Context triple: [Library of Congress Subject Headings, hasApplicationArea, academic research]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasPolicyArea
Indicates that an entity (such as a policy, program, or initiative) is associated with or pertains to a specific policy area or domain.
-
C.
appliesTo
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
D.
hasApplicationDomain
chosen
Indicates that something is associated with, used in, or relevant to a particular field, area, or domain of application.
-
E.
appliesAt
Indicates that an action, rule, or condition is relevant to or in effect at a specific location, context, or point in time.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea4aa16881909b2c8404b85992df |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e9428098819089d5950cd2c96dc4 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:07 p.m.