Triple
T27389091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villetaneuse |
E691468
|
entity |
| Predicate | universityCampusNameChange |
P81541
|
FINISHED |
| Object | Université Paris 13 renamed Université Sorbonne Paris Nord |
—
|
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: Université Paris 13 renamed Université Sorbonne Paris Nord | Statement: [Villetaneuse, universityCampusNameChange, Université Paris 13 renamed Université Sorbonne Paris Nord]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: universityCampusNameChange Context triple: [Villetaneuse, universityCampusNameChange, Université Paris 13 renamed Université Sorbonne Paris Nord]
-
A.
universityFormerName
chosen
Indicates that a university previously had a different official name before adopting its current one.
-
B.
campusName
Indicates the official name assigned to a particular campus.
-
C.
campusMovedTo
Indicates that the location of a campus has been relocated from one place to another.
-
D.
campusNameOrigin
Indicates the source or reason behind how a campus received its name.
-
E.
universityName
Indicates the official name associated with a particular university.
- 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_69ef520386788190bc92cfcd97ebb67a |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c663be481908f233d25d28713a4 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 12:25 p.m.