Triple
T11672249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhuyin |
E277409
|
entity |
| Predicate | learningRole |
P100701
|
FINISHED |
| Object | bridge to full character literacy |
—
|
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: bridge to full character literacy | Statement: [Zhuyin, learningRole, bridge to full character literacy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learningRole Context triple: [Zhuyin, learningRole, bridge to full character literacy]
-
A.
pretrainingRole
Indicates the role or function an entity serves specifically during a pretraining phase or process.
-
B.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
C.
evaluationRole
Indicates the role or capacity in which an entity participates in an evaluation or assessment process.
-
D.
roleInTrain
Indicates the specific function or position an entity holds within the context of a train (e.g., passenger, conductor, locomotive, or car type).
-
E.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a443b6848190a1eb6825fbc49d08 |
completed | April 10, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69d88a77e6e88190b7519100bde76575 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d8938a1f8c81908ffb049fa5fee5a7 |
completed | April 10, 2026, 6:07 a.m. |
Created at: April 8, 2026, 9:40 p.m.