Triple
T4370906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 100BASE-X |
E98892
|
entity |
| Predicate | maximumSegmentLengthDependsOn |
P15978
|
FINISHED |
| Object | medium type |
—
|
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: medium type | Statement: [100BASE-X, maximumSegmentLengthDependsOn, medium type]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumSegmentLengthDependsOn Context triple: [100BASE-X, maximumSegmentLengthDependsOn, medium type]
-
A.
maximumSegmentLength
chosen
Indicates the greatest allowable or observed length of a segment within a given context or structure.
-
B.
maximumNumberOfSegments
Indicates the greatest allowable or observed count of discrete segments into which something can be or is divided.
-
C.
maximumStationsPerSegment
Indicates the greatest number of stations that are allowed or can exist within a single segment.
-
D.
maximumConsecutiveTerms
Indicates the greatest number of terms that can occur in an unbroken, continuous sequence within a given context or structure.
-
E.
isFeatureLength
Indicates that something (typically a film or video) has a duration long enough to be considered a full-length, standard feature.
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3521cb7ec8190b7b79675871d97d8 |
completed | March 12, 2026, 11:54 p.m. |
| PD | Predicate disambiguation | batch_69b34f53e3cc8190bf5d4dbe2413bf65 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:17 p.m.