Triple
T12911284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Starfire |
E308869
|
entity |
| Predicate | languageLearningMethod |
P40422
|
FINISHED |
| Object | physical contact (kissing) |
—
|
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: physical contact (kissing) | Statement: [Starfire, languageLearningMethod, physical contact (kissing)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageLearningMethod Context triple: [Starfire, languageLearningMethod, physical contact (kissing)]
-
A.
learnsLanguageFrom
Indicates that one entity acquires or improves knowledge of a language through instruction, exposure, or guidance provided by another entity.
-
B.
languageRevivalMethod
Indicates the method or strategy used to revive or revitalize a language that is endangered, dormant, or no longer actively spoken.
-
C.
studyMethods
chosen
Indicates that an entity uses or engages in particular methods or techniques for studying.
-
D.
structureLearning
Indicates a process in which an agent infers or constructs the underlying structure or dependency relationships within a set of variables, data, or a model.
-
E.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
- 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9719f96248190b746f9d4a468560c |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:41 p.m.