Triple
T5866032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USF Dons |
E130395
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object | Don |
E75078
|
NE 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: Don | Statement: [USF Dons, mascot, Don]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Context triple: [USF Dons, mascot, Don]
-
A.
Don
The Don is a major river in southwestern Russia that flows from the Central Russian Upland to the Sea of Azov, historically serving as an important trade route and cultural boundary.
-
B.
Don
chosen
Don is a masculine given name, often a short form of Donald, used in English-speaking countries.
-
C.
Danny
Danny is the young boy protagonist of the science-fiction adventure film "Zathura: A Space Adventure," whose discovery of a mysterious board game launches the story’s intergalactic journey.
-
D.
Danny
Danny is the young, psychically gifted son of Jack Torrance in Stephen King’s horror novel "The Shining" and its film adaptations.
-
E.
Danny
Danny is the charismatic, hard-drinking World War I veteran whose inherited houses and loose community of friends drive the picaresque adventures in John Steinbeck’s novel "Tortilla Flat."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c0085047dc8190af24e311edad3c07 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c035bfa8188190ab0e28101fdf5e6f |
completed | March 22, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0bfe3d3d88190853f76e41c61ad71 |
completed | March 23, 2026, 4:21 a.m. |
Created at: March 22, 2026, 3:56 p.m.