Triple
T15322423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flinders University Museum of Art |
E366323
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object |
FUMA
FUMA is the art museum of Flinders University in South Australia, known for its diverse collections of Australian, Aboriginal, and international art.
|
E1150151
|
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: FUMA | Statement: [Flinders University Museum of Art, hasAlternativeName, FUMA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FUMA Context triple: [Flinders University Museum of Art, hasAlternativeName, FUMA]
-
A.
FUM
FUM is an abbreviation for Ferdowsi University of Mashhad, a major public research university in Mashhad, Iran.
-
B.
FUGA
FUGA is a global music distribution and services company that provides digital delivery, rights management, and marketing solutions for record labels and independent artists.
-
C.
Fumusa
Fumusa is an Italian-origin surname most notably associated with American actor Dominic Fumusa.
-
D.
FMA
FMA is an acronym commonly used for Fault Management Architecture, a system framework for detecting, diagnosing, and resolving hardware and software faults in computing environments.
-
E.
FAMO
FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
- 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: FUMA Triple: [Flinders University Museum of Art, hasAlternativeName, FUMA]
Generated description
FUMA is the art museum of Flinders University in South Australia, known for its diverse collections of Australian, Aboriginal, and international art.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FUMA Target entity description: FUMA is the art museum of Flinders University in South Australia, known for its diverse collections of Australian, Aboriginal, and international art.
-
A.
FUM
FUM is an abbreviation for Ferdowsi University of Mashhad, a major public research university in Mashhad, Iran.
-
B.
FUGA
FUGA is a global music distribution and services company that provides digital delivery, rights management, and marketing solutions for record labels and independent artists.
-
C.
Fumusa
Fumusa is an Italian-origin surname most notably associated with American actor Dominic Fumusa.
-
D.
FMA
FMA is an acronym commonly used for Fault Management Architecture, a system framework for detecting, diagnosing, and resolving hardware and software faults in computing environments.
-
E.
FAMO
FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd460288190b5c41f0a0aeee949 |
completed | April 16, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8aaef608190bd3ec9fdd215afbb |
completed | May 9, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_69fefa435efc81908c1e88267e745cdd |
completed | May 9, 2026, 9:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fefb2ee9108190b3d8633cc9713c7b |
completed | May 9, 2026, 9:15 a.m. |
Created at: April 10, 2026, 3:16 a.m.