Triple
T107722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorbonne University |
E2175
|
entity |
| Predicate | hasUrbanCampus |
P110
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Sorbonne University, hasUrbanCampus, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanCampus Context triple: [Sorbonne University, hasUrbanCampus, true]
-
A.
hasPublicUniversityCampus
Indicates that a public university maintains or operates a campus at the specified location.
-
B.
hasMainCampus
Indicates that an educational institution is primarily based at or chiefly associated with a particular campus location.
-
C.
hasAdditionalCampus
Indicates that an educational institution maintains one or more campuses in addition to its primary or main campus.
-
D.
hasMajorUniversity
Indicates that a location or region contains at least one prominent, large, or academically significant university.
-
E.
campusType
chosen
Indicates the classification or category of a campus based on its type (e.g., main, satellite, urban, rural).
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25a1199ac8190ac65ffaaf45b4f5b |
completed | Feb. 28, 2026, 2:59 a.m. |
| PD | Predicate disambiguation | batch_69a2563e7188819091e9a94e071991d7 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.