Triple
T36351093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Des Bishop: Breaking China |
E895204
|
entity |
| Predicate | featuresLanguageLearning |
P36232
|
FINISHED |
| Object | Mandarin Chinese |
E181408
|
NE 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: Mandarin Chinese | Statement: [Des Bishop: Breaking China, featuresLanguageLearning, Mandarin Chinese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresLanguageLearning Context triple: [Des Bishop: Breaking China, featuresLanguageLearning, Mandarin Chinese]
-
A.
learnsLanguage
Indicates that an entity acquires knowledge or skill in a particular language through study or practice.
-
B.
focusesOnLanguage
Indicates that an entity’s primary attention, activity, or content is directed toward language as its main subject or concern.
-
C.
learnsLanguageFrom
Indicates that one entity acquires or improves knowledge of a language through instruction, exposure, or guidance provided by another entity.
-
D.
languageOfScientificLearning
Indicates the language in which scientific knowledge or instruction is taught, communicated, or acquired.
-
E.
languageOfTeachings
chosen
Indicates the language in which teachings, lessons, or instructional content are delivered or expressed.
- F. None of above.
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_69f76e4f437c8190a1af3ea2564f41f5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a39a316b9d08190acfc36f531dc7b70 |
completed | June 22, 2026, 9:03 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:09 p.m.