Triple
T33131098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Learjet 85 |
E847870
|
entity |
| Predicate | intendedRange_nm |
P25150
|
FINISHED |
| Object | 2600 |
—
|
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: 2600 | Statement: [Learjet 85, intendedRange_nm, 2600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intendedRange_nm Context triple: [Learjet 85, intendedRange_nm, 2600]
-
A.
rangeOf
Indicates that one entity specifies the set of possible values (range) that another entity’s outputs or properties can take.
-
B.
tieneRango
Indicates that one entity possesses, is assigned, or falls within a specific rank or range in relation to another entity.
-
C.
hasRange
Indicates that a property or relation is constrained to take its values from a specified class, type, or value set.
-
D.
coreRange
Indicates the primary spatial or temporal extent within which an entity, phenomenon, or relationship is predominantly present or valid.
-
E.
operationalRange
chosen
Indicates the span of conditions (such as distance, time, or environment) within which a system, device, or process can function effectively and safely.
- 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_69f349588f088190b7c9588860f72033 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fd553d7cb881908d243e7a9f30ac85 |
completed | May 8, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69fd514dcb1c81908333c70d7edd79c9 |
completed | May 8, 2026, 2:58 a.m. |
Created at: May 1, 2026, 1:27 a.m.