Triple
T20131832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reports and addresses on university education |
E490912
|
entity |
| Predicate | hasPhysicalExtent |
P20336
|
FINISHED |
| Object | approximately 300 pages |
—
|
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: approximately 300 pages | Statement: [Reports and addresses on university education, hasPhysicalExtent, approximately 300 pages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhysicalExtent Context triple: [Reports and addresses on university education, hasPhysicalExtent, approximately 300 pages]
-
A.
hasPhysicalMedium
Indicates that one entity serves as the tangible carrier or material form through which another entity exists, is stored, or is transmitted.
-
B.
hasPhysicalFootprint
Indicates that one entity occupies or affects a specific physical area or space in the real world.
-
C.
hasApproximateExtent
chosen
Indicates that one entity has a spatial, temporal, or quantitative extent that is only roughly or approximately specified rather than exact.
-
D.
hasPhysicalFamily
Indicates that one entity is related to another as a member of the same biological or legally recognized family.
-
E.
hasPhysicalLocationType
Indicates that an entity is associated with a specific kind or category of physical location (e.g., building type, facility type, or place type).
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66762f0448190b7dbbc665e179ffc |
completed | April 20, 2026, 5:50 p.m. |
| PD | Predicate disambiguation | batch_69e54cfb0d0081908e789b9b57e96668 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:31 p.m.