Triple
T7036979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evia |
E163408
|
entity |
| Predicate | widthRangeApprox |
P12465
|
FINISHED |
| Object | from 6 km to 50 km |
—
|
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: from 6 km to 50 km | Statement: [Evia, widthRangeApprox, from 6 km to 50 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: widthRangeApprox Context triple: [Evia, widthRangeApprox, from 6 km to 50 km]
-
A.
weightRangeDescription
Indicates the textual description that specifies the range within which an entity’s weight falls.
-
B.
hasDimensionsApprox
Indicates that an entity has physical dimensions that are known only approximately, rather than as exact measurements.
-
C.
typicalRange
Indicates the usual or expected range of values, conditions, or states within which something normally occurs or applies.
-
D.
heightApproximateFeet
Indicates that one entity’s height is approximately equal to a specified value measured in feet.
-
E.
includesSizeRange
chosen
Indicates that one entity specifies or covers a particular range of sizes associated with another entity.
- 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_69c6885e7c1c8190be32a8f79ab4e0cf |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e4a3c36c819080942c59f1830ae8 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bb602081908bfa6186a1f5a4b4 |
completed | March 27, 2026, 7:59 p.m. |
Created at: March 27, 2026, 2:36 p.m.