Triple
T15053571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Junio C Hamano |
E379428
|
entity |
| Predicate | licenseContext |
P77009
|
FINISHED |
| Object | GPL-licensed 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: GPL-licensed software | Statement: [Junio C Hamano, licenseContext, GPL-licensed software]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: licenseContext Context triple: [Junio C Hamano, licenseContext, GPL-licensed software]
-
A.
license
Indicates that one entity has granted another entity formal permission or authorization to use, perform, or exploit something under specified terms.
-
B.
licenseFocus
chosen
Indicates that a license specifically targets, applies to, or is primarily concerned with a particular subject, activity, or scope.
-
C.
licenseScope
Indicates the specific rights, limitations, and conditions that define how and where a license may be used or applied.
-
D.
licenseConcern
Indicates that there is an issue, risk, or consideration related to a license associated with the entities involved.
-
E.
licenseStewardship
Indicates that one entity is responsible for managing, overseeing, or administering a license on behalf of another entity.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda92091c81909180f486edf01405 |
completed | April 15, 2026, 12:23 a.m. |
| PD | Predicate disambiguation | batch_69deb95a182081908fffc4402b02a394 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:01 a.m.