Triple
T13318982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | InCites |
E317264
|
entity |
| Predicate | granularityLevel |
P109501
|
FINISHED |
| Object | institution |
—
|
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: institution | Statement: [InCites, granularityLevel, institution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: granularityLevel Context triple: [InCites, granularityLevel, institution]
-
A.
controlGranularity
Indicates the level of detail or fineness with which control or regulation is applied within a given process or system.
-
B.
securityGranularity
Indicates the level of detail or specificity at which security controls, permissions, or protections are defined and applied within a system or context.
-
C.
translationGranularity
Indicates the level of detail or segmentation at which a translation is produced or aligned between source and target content.
-
D.
encryptionGranularity
Indicates the level of detail or scope at which data is encrypted within a system or process.
-
E.
accessGranularity
Indicates the level of detail or scope at which access or permissions are defined and applied within a system or resource.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6babd88190a5d529df9584b9a4 |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99cf7f9c48190a6a4f452b4a2aefa |
completed | April 11, 2026, 12:59 a.m. |
Created at: April 9, 2026, 9:29 p.m.