Triple
T34382156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Yoon-ok |
E882465
|
entity |
| Predicate | almaMaterLocation |
P86973
|
FINISHED |
| Object | Seoul, South Korea |
—
|
NE NERFINISHED |
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: Seoul, South Korea | Statement: [Kim Yoon-ok, almaMaterLocation, Seoul, South Korea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: almaMaterLocation Context triple: [Kim Yoon-ok, almaMaterLocation, Seoul, South Korea]
-
A.
universityLocatedIn
Indicates that a university is situated within or associated with a specific geographic location or administrative region.
-
B.
servesAsAlmaMaterOf
Indicates that an educational institution is the alma mater of a person or entity, meaning they previously studied or graduated there.
-
C.
firstTaughtAt
Indicates the institution or place where an entity (typically a person) began their teaching career or taught for the first time.
-
D.
educationLocation
chosen
Indicates the place or institution where an entity received education or underwent formal learning.
-
E.
cityOfInstitution
Indicates the city in which an institution is located or based.
- F. None of above.
Provenance (3 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_69f349c0219881909393bbbc1edc8161 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:59 a.m.