Triple
T245937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Project Ozma |
E5036
|
entity |
| Predicate | dataAnalysisType |
P4241
|
FINISHED |
| Object | search for narrowband, non-natural radio emissions |
—
|
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: search for narrowband, non-natural radio emissions | Statement: [Project Ozma, dataAnalysisType, search for narrowband, non-natural radio emissions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dataAnalysisType Context triple: [Project Ozma, dataAnalysisType, search for narrowband, non-natural radio emissions]
-
A.
analyzes
Indicates that one entity systematically examines or evaluates another entity to understand its nature, structure, or components.
-
B.
dataTypeCollected
chosen
Indicates that a specific type or category of data is gathered or recorded in the context of an entity or process.
-
C.
hasRegressionAnalysis
Indicates that a regression analysis has been performed on, or is associated with, a given dataset, model, or relationship between variables.
-
D.
dataUse
Indicates how data is intended to be accessed, processed, or applied within a particular context or activity.
-
E.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d128c0081909908825b302ae635 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b63b0bc8190864d7324d339fb48 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.