Triple
T26096554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cia-Cia |
E658283
|
entity |
| Predicate | hasEducationExperiment |
P165842
|
FINISHED |
| Object | use of Hangul in local school textbooks |
—
|
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: use of Hangul in local school textbooks | Statement: [Cia-Cia, hasEducationExperiment, use of Hangul in local school textbooks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEducationExperiment Context triple: [Cia-Cia, hasEducationExperiment, use of Hangul in local school textbooks]
-
A.
hasEducationalStatus
Indicates that an entity possesses a particular level, state, or condition of formal education or academic attainment.
-
B.
hasEducationIn
Indicates that an entity has received education, training, or formal study in a specified field, subject, or discipline.
-
C.
hasStatusInEducation
Indicates that an entity holds a particular educational status or standing within an educational system, program, or institution.
-
D.
hasEducationRight
Indicates that an entity possesses a legal or moral entitlement to receive education.
-
E.
hasEducationalProfile
Indicates that an entity is associated with a specific educational profile, detailing its education-related characteristics, qualifications, or academic background.
- 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_69ee5bbfc4d08190a1b206d0ac3a1e8d |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f65b14512c8190a40e70319dcc54cd |
completed | May 2, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69f659cc571c819097e51e531961d812 |
completed | May 2, 2026, 8:08 p.m. |
| PDg | Predicate description generation | batch_69f65a9cb0bc8190bf8a9b319900bad5 |
completed | May 2, 2026, 8:12 p.m. |
Created at: April 26, 2026, 7:51 p.m.