Triple
T25301301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korea women's national handball team |
E634349
|
entity |
| Predicate | statusInAsia |
P33417
|
FINISHED |
| Object | leading women's handball team |
—
|
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: leading women's handball team | Statement: [South Korea women's national handball team, statusInAsia, leading women's handball team]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: statusInAsia Context triple: [South Korea women's national handball team, statusInAsia, leading women's handball team]
-
A.
statusInJapan
Indicates the legal, social, or official standing or condition an entity holds specifically within the context of Japan.
-
B.
locatedInAsia
Indicates that the subject entity is geographically situated within the continent of Asia.
-
C.
statusInRegion
chosen
Indicates the specific status or condition an entity holds within a particular geographic or administrative region.
-
D.
statusInIndonesia
Indicates the legal, social, or operational standing or condition of an entity specifically within the context of Indonesia.
-
E.
statusInThailand
Indicates the legal, social, or official standing or condition an entity holds specifically within the context of Thailand.
- 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_69e75a972c6481909bc11710e8d30a6c |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 21, 2026, 1:24 p.m.