Triple
T25056845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeriba Shigan |
E627541
|
entity |
| Predicate | learnsLanguage |
P157589
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Jeriba Shigan, learnsLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learnsLanguage Context triple: [Jeriba Shigan, learnsLanguage, English]
-
A.
learnsLanguageFrom
Indicates that one entity acquires or improves knowledge of a language through instruction, exposure, or guidance provided by another entity.
-
B.
languageAcquisitionContext
Indicates the situational or environmental context in which an entity learns or acquires a language.
-
C.
languageOfDevelopment
Indicates the programming or natural language used to develop, implement, or create a given entity.
-
D.
focusesOnLanguage
Indicates that an entity’s primary attention, activity, or content is directed toward language as its main subject or concern.
-
E.
learnedIn
Indicates that an entity acquired knowledge, skills, or information within a particular context, environment, or source.
- 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_69e2ff2c45f48190afa28369f1df6786 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f459958c908190a377bb3cf34b1b28 |
completed | May 1, 2026, 7:43 a.m. |
| PD | Predicate disambiguation | batch_69f442c861188190967655c6d8012380 |
completed | May 1, 2026, 6:06 a.m. |
| PDg | Predicate description generation | batch_69f448fe11f08190bdd53ca7ba2d51e4 |
completed | May 1, 2026, 6:32 a.m. |
Created at: April 18, 2026, 6:09 a.m.