Triple
T8703861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Sweet |
E206596
|
entity |
| Predicate | softwareLicenseWorkedWith |
P84641
|
FINISHED |
| Object | open-source licenses |
—
|
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: open-source licenses | Statement: [Michael Sweet, softwareLicenseWorkedWith, open-source licenses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: softwareLicenseWorkedWith Context triple: [Michael Sweet, softwareLicenseWorkedWith, open-source licenses]
-
A.
softwareLibrary
Indicates that one entity is a software library that provides code, functionality, or services used by another entity.
-
B.
productTypeWorkedOn
Indicates that an entity has worked on or been involved in creating, developing, or handling a particular type of product.
-
C.
softwareLicensingAdvocacy
Indicates advocacy efforts aimed at promoting, defending, or influencing policies and practices related to software licensing.
-
D.
supportsLicense
Indicates that one entity is compatible with, enables, or is configured to work under a specified license.
-
E.
targetedSoftware
Indicates that a particular piece of software is the specific focus or target of an action, operation, or effect.
- 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_69ca835645e881908f00e3c8b51da81d |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc58fa0a208190a520e0e1f7faaea9 |
completed | March 31, 2026, 11:30 p.m. |
| PD | Predicate disambiguation | batch_69cc456bda508190a9aa0fb92760739e |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc582412f48190ae819965bfb0e75d |
completed | March 31, 2026, 11:26 p.m. |
Created at: March 30, 2026, 6:34 p.m.