Triple
T380999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GNU Pascal |
E8677
|
entity |
| Predicate | softwareModel |
P12726
|
FINISHED |
| Object | free and open-source software |
—
|
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: free and open-source software | Statement: [GNU Pascal, softwareModel, free and open-source software]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: softwareModel Context triple: [GNU Pascal, softwareModel, free and open-source software]
-
A.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
B.
modelNumber
Indicates that one entity is the specific model identifier or code assigned to another entity (such as a product or device).
-
C.
deploymentModel
Indicates the type or manner in which a system, service, or resource is deployed or made available (e.g., on-premises, cloud, hybrid).
-
D.
operatingModel
Indicates how an organization structures and manages its processes, resources, and governance to deliver its products or services.
-
E.
architecture
Indicates the structural design or organizational framework that defines how components of a system or entity are arranged and interact.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec2c95088190a603bb1ee076ebd6 |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e964d4b481909290e474b0341e3c |
completed | Feb. 28, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69a2eae0bd7081908197bbf5c55fe647 |
completed | Feb. 28, 2026, 1:17 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.