Triple
T4276959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jupyter Notebook |
E97066
|
entity |
| Predicate | supportsNarrativeText |
P55148
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Jupyter Notebook, supportsNarrativeText, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsNarrativeText Context triple: [Jupyter Notebook, supportsNarrativeText, true]
-
A.
supportsNarrativeOf
Indicates that one entity provides evidence, context, or structure that upholds, reinforces, or advances the storyline or interpretive account expressed by another entity.
-
B.
containsNarrativeOf
Indicates that one entity includes or presents the story, account, or narrative content of another entity.
-
C.
hasNarrative
Indicates that one entity contains, presents, or is associated with a story or narrative about another entity or subject.
-
D.
hasNarration
Indicates that an entity provides spoken or written commentary or storytelling for another entity, such as a work, event, or scene.
-
E.
narrativeFrame
Indicates the overarching narrative context or perspective within which events, actions, or relationships are presented or interpreted.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501d677481909e7416a1d2b0008c |
completed | March 12, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69b347faa45481908c19c29fb906dc92 |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e0606488190baadf469a1afc3c2 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:07 p.m.