Triple
T4001202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Very Large Array |
E89416
|
entity |
| Predicate | hasFrequencyCoverage |
P53436
|
FINISHED |
| Object | approximately 1 to 50 GHz |
—
|
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: approximately 1 to 50 GHz | Statement: [Very Large Array, hasFrequencyCoverage, approximately 1 to 50 GHz]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrequencyCoverage Context triple: [Very Large Array, hasFrequencyCoverage, approximately 1 to 50 GHz]
-
A.
isFrequentlyCovered
Indicates that an entity is regularly or commonly reported on, discussed, or featured, especially in media or informational sources.
-
B.
hasCoverage
Indicates that one entity provides insurance or protection coverage for another entity or subject.
-
C.
dataCoverage
Indicates the extent or proportion of relevant data that is included, captured, or represented within a given dataset or system.
-
D.
hasFrequencyCategory
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
E.
providesCoverage
Indicates that one entity supplies protection, insurance, or service coverage to another entity or for a specified risk or scope.
- 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_69aed9585e788190bec2d39deba3750f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa8579288190940487ad07e38de0 |
completed | March 9, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69aef8f89f2881909b0965419d15d46c |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefa815f2c8190818c9ffd9d1bf478 |
completed | March 9, 2026, 4:51 p.m. |
Created at: March 9, 2026, 3:34 p.m.