Triple
T9187406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fast File System |
E220493
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | Commodore |
E202460
|
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: Commodore | Statement: [Fast File System, developer, Commodore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Commodore Context triple: [Fast File System, developer, Commodore]
-
A.
Commodore
chosen
Commodore was a pioneering computer company best known for its influential home computers like the Commodore 64 and the Amiga line.
-
B.
Admiral
Admiral is an Austrian sports betting and gaming company known for its prominent sponsorships in professional football and other sports.
-
C.
Admiral
Admiral is a senior naval officer rank, typically the highest or among the highest in a navy, responsible for commanding large fleets and holding top-level strategic leadership roles.
-
D.
Commodore Hansteen
Commodore Hansteen is a fictional senior space-rescue officer in Arthur C. Clarke’s science fiction novel "A Fall of Moondust."
-
E.
Admiral Grant
Admiral Grant is a fictional high-ranking naval officer portrayed by actor John Amos.
- 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_69ca83e6d77c81909862b7afef56b1bf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccc31bd6f88190b2ea644420995e41 |
completed | April 1, 2026, 7:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05c226bc881909609da0bfbd4748e |
completed | April 4, 2026, 12:32 a.m. |
Created at: March 30, 2026, 7:24 p.m.