Triple
T5696374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Ferrara |
E125549
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
UniFE
UniFE is the commonly used abbreviation for the University of Ferrara, a public research university located in Ferrara, Italy.
|
E542061
|
NE FINISHED |
How this triple was built (4 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: UniFE | Statement: [University of Ferrara, shortName, UniFE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UniFE Context triple: [University of Ferrara, shortName, UniFE]
-
A.
UniBE
UniBE is the commonly used abbreviation for the University of Bern, a major public research university located in Bern, Switzerland.
-
B.
UniPG
UniPG is the commonly used abbreviation for the University of Perugia, a historic Italian public university located in Perugia, Umbria.
-
C.
UniGe
UniGe is the commonly used abbreviation for the University of Genoa, a major public research university located in Genoa, Italy.
-
D.
UniFR
UniFR is the commonly used abbreviation for the University of Fribourg, a bilingual (French and German) public university in Fribourg, Switzerland.
-
E.
UNICA
UNICA is a network of universities from the capitals of Europe that promotes academic collaboration, mobility, and shared higher education policies among its member institutions.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: UniFE Triple: [University of Ferrara, shortName, UniFE]
Generated description
UniFE is the commonly used abbreviation for the University of Ferrara, a public research university located in Ferrara, Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: UniFE Target entity description: UniFE is the commonly used abbreviation for the University of Ferrara, a public research university located in Ferrara, Italy.
-
A.
UniBE
UniBE is the commonly used abbreviation for the University of Bern, a major public research university located in Bern, Switzerland.
-
B.
UniPG
UniPG is the commonly used abbreviation for the University of Perugia, a historic Italian public university located in Perugia, Umbria.
-
C.
UniGe
UniGe is the commonly used abbreviation for the University of Genoa, a major public research university located in Genoa, Italy.
-
D.
UniFR
UniFR is the commonly used abbreviation for the University of Fribourg, a bilingual (French and German) public university in Fribourg, Switzerland.
-
E.
UNICA
UNICA is a network of universities from the capitals of Europe that promotes academic collaboration, mobility, and shared higher education policies among its member institutions.
- F. None of above. chosen
Provenance (5 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0240bde1881909f7ea13bd84deaa8 |
completed | March 22, 2026, 5:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a5635448190ada625283405752f |
completed | March 22, 2026, 9:08 p.m. |
| NEDg | Description generation | batch_69c05be7f7cc8190bb1f8081289c5e02 |
completed | March 22, 2026, 9:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0620ee1848190935f5f78abbed7ba |
completed | March 22, 2026, 9:41 p.m. |
Created at: March 22, 2026, 3:45 p.m.