Triple
T19241852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UniGe |
E481151
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | UniGe |
—
|
NE NERFINISHED |
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: UniGe | Statement: [UniGe, shortName, UniGe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UniGe Context triple: [UniGe, shortName, UniGe]
-
A.
UniGe
chosen
UniGe is the commonly used abbreviation for the University of Genoa, a major public research university located in Genoa, Italy.
-
B.
UniBE
UniBE is the commonly used abbreviation for the University of Bern, a major public research university located in Bern, Switzerland.
-
C.
UniPG
UniPG is the commonly used abbreviation for the University of Perugia, a historic Italian public university located in Perugia, Umbria.
-
D.
UniFE
UniFE is the commonly used abbreviation for the University of Ferrara, a public research university located in Ferrara, Italy.
-
E.
UniSI
UniSI is the commonly used abbreviation for the University of Siena, a historic Italian university located in Tuscany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5faf2353c819094a9a1af3a858715 |
completed | April 20, 2026, 10:07 a.m. |
Created at: April 10, 2026, 1:27 p.m.