Triple
T1724680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FAT16 |
E37469
|
entity |
| Predicate | fileNameCharacterSet |
P7661
|
FINISHED |
| Object | limited ASCII subset |
—
|
LITERAL 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: limited ASCII subset | Statement: [FAT16, fileNameCharacterSet, limited ASCII subset]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fileNameCharacterSet Context triple: [FAT16, fileNameCharacterSet, limited ASCII subset]
-
A.
usesCharacterSet
chosen
Indicates that one entity employs or relies on a specific character set defined by another entity for encoding or representing text.
-
B.
characterSetType
Indicates the type or category of character set associated with or used by an entity.
-
C.
characterSetName
Indicates the name assigned to a particular character set used for encoding or representing characters.
-
D.
codingSystemType
Indicates the classification or category of coding system used to encode or represent information in a given context.
-
E.
hasUnicodeName
Indicates that an entity is associated with a specific official Unicode name assigned to a character or symbol.
- F. None of above.
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_69a8861acab88190bb43cde203429399 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aadb7bda1081908f2c41c520c9c55c |
completed | March 6, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69aa61c0a0288190bce9d60062a84b69 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:30 p.m.