Triple
T34594851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brett Cannon |
E888280
|
entity |
| Predicate | hasTalkedAbout |
P3281
|
FINISHED |
| Object | Python governance model |
—
|
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: Python governance model | Statement: [Brett Cannon, hasTalkedAbout, Python governance model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTalkedAbout Context triple: [Brett Cannon, hasTalkedAbout, Python governance model]
-
A.
hasSpokenAbout
chosen
Indicates that one entity has verbally expressed, discussed, or mentioned another entity or topic.
-
B.
spokenBefore
Indicates that one entity has spoken or produced speech earlier in time than another entity.
-
C.
hasPresented
Indicates that one entity has formally given, delivered, or shown something (such as information, a work, or an award) to another entity.
-
D.
hasWrittenAbout
Indicates that one entity has authored content or material discussing, analyzing, or referencing another entity.
-
E.
hasHad
Indicates that an entity previously experienced, possessed, or was involved in something at some point in the past.
- 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_69f349d3bfcc81909874c99e646fb3ea |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7465687bc8190a9da44d62b634ed7 |
completed | May 3, 2026, 12:57 p.m. |
| PD | Predicate disambiguation | batch_69f743f4ceb08190a21fe7f4a99b166b |
completed | May 3, 2026, 12:47 p.m. |
Created at: May 1, 2026, 2:03 a.m.