Triple
T12556729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nokia 3100 |
E295235
|
entity |
| Predicate | supportsCustomCovers |
P105880
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Nokia 3100, supportsCustomCovers, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsCustomCovers Context triple: [Nokia 3100, supportsCustomCovers, yes]
-
A.
hasCoverType
Indicates that one entity possesses or is associated with a specific type or category of cover.
-
B.
hasCoverFeature
Indicates that one entity serves as a prominent or featured element on the cover of another entity (such as a publication, product, or media item).
-
C.
containsCoverOf
Indicates that one entity includes within it a cover or covering representation of another entity.
-
D.
hasUniversalCover
Indicates that one mathematical space serves as the universal covering space of another, mapping onto it via a covering map that is simply connected and covers all its loops.
-
E.
coversMode
Indicates that one entity includes, addresses, or supports a particular mode or manner of operation associated with another entity.
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95f5507b481908d13cc317b7402f6 |
completed | April 10, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69d95410d0b0819097646edd1b837104 |
completed | April 10, 2026, 7:48 p.m. |
| PDg | Predicate description generation | batch_69d95f5148948190946a575d812b329d |
completed | April 10, 2026, 8:36 p.m. |
Created at: April 8, 2026, 11:47 p.m.