Triple
T1773929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MFS |
E38935
|
entity |
| Predicate | maximumFileNameLength |
P31494
|
FINISHED |
| Object | 63 characters |
—
|
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: 63 characters | Statement: [MFS, maximumFileNameLength, 63 characters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumFileNameLength Context triple: [MFS, maximumFileNameLength, 63 characters]
-
A.
volumeIdentifierLengthLimit
Indicates the maximum allowed length for a volume’s identifier within a given system or context.
-
B.
maximumNumberOfSegments
Indicates the greatest allowable or observed count of discrete segments into which something can be or is divided.
-
C.
maximumVolumeSize
Indicates the largest allowable size or capacity that a volume can have within a given system or context.
-
D.
maximumSegmentLength
Indicates the greatest allowable or observed length of a segment within a given context or structure.
-
E.
maximumRecordedLength
Indicates the greatest length value that has been observed and recorded for the entity in question.
- F. None of above. chosen
Provenance (4 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab17e368048190b7b73d156400f772 |
completed | March 6, 2026, 6:07 p.m. |
| PD | Predicate disambiguation | batch_69aa61cd4c1c8190a8dff391f5642bfe |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab17d0a644819087e6ce39d6c60da5 |
completed | March 6, 2026, 6:07 p.m. |
Created at: March 4, 2026, 7:31 p.m.