Triple
T262190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China |
E5561
|
entity |
| Predicate | literacyStatus |
P10080
|
FINISHED |
| Object | high literacy rate |
—
|
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: high literacy rate | Statement: [China, literacyStatus, high literacy rate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literacyStatus Context triple: [China, literacyStatus, high literacy rate]
-
A.
sociolinguisticStatus
Indicates the social and cultural standing or prestige associated with a language variety or linguistic feature within a particular community or context.
-
B.
educatedIn
Indicates that an entity received education or formal training at a specified institution or place.
-
C.
educatedAt
Indicates that an entity received education or formal training at a specified institution or place of learning.
-
D.
languageOfSignage
Indicates the language used on signs or written displays associated with an entity.
-
E.
academicStatus
Indicates the educational or scholarly standing or level an entity holds within an academic context.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e2aba74819093eddd8d820260c0 |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b6c968c819094fc903a3a377e15 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25e292fdc8190bfd51d8848f9ed58 |
completed | Feb. 28, 2026, 3:16 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.