Triple
T34540334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python 3.11 |
E886788
|
entity |
| Predicate | introducesModule |
P195787
|
FINISHED |
| Object | tomllib |
—
|
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: tomllib | Statement: [Python 3.11, introducesModule, tomllib]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: introducesModule Context triple: [Python 3.11, introducesModule, tomllib]
-
A.
introducesCore
Indicates that one entity presents or brings another entity into a central, foundational, or primary part of a system, concept, or structure.
-
B.
introducesSystem
Indicates that an entity presents, brings into use, or makes known a particular system to others.
-
C.
introducesName
Indicates that one entity presents or gives a name or designation to another entity.
-
D.
module2Content
Indicates that one entity contains or provides the instructional or informational material that makes up the second module in a sequence or course.
-
E.
introducesAlgorithm
Indicates that an entity presents or brings a specific algorithm into use, awareness, or discussion.
- 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_69f349ce5eb881909e431c670944aa68 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69fde5d677b88190bc904e6df8617c18 |
completed | May 8, 2026, 1:32 p.m. |
Created at: May 1, 2026, 2:02 a.m.