Triple
T590103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 10BASE-FL |
E17247
|
entity |
| Predicate | maximumSegmentLength |
P15978
|
FINISHED |
| Object | 2000 meters |
—
|
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: 2000 meters | Statement: [10BASE-FL, maximumSegmentLength, 2000 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumSegmentLength Context triple: [10BASE-FL, maximumSegmentLength, 2000 meters]
-
A.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
B.
maximumReach
Indicates the greatest extent, distance, or limit that something can reach or influence within a given context.
-
C.
isFeatureLength
Indicates that something (typically a film or video) has a duration long enough to be considered a full-length, standard feature.
-
D.
maximumDepth
Indicates the greatest extent or deepest level reached by something within a given context or structure.
-
E.
hasMainSpanLength
Indicates the relationship specifying the primary or main span’s length associated with an entity.
- 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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bb8ff0081909cd53d88930e2693 |
completed | March 1, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69a494cc13988190892ca10bd7ae9f09 |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a4985ada988190aaea628a9b55bca4 |
completed | March 1, 2026, 7:49 p.m. |
Created at: March 1, 2026, 7:33 p.m.