Triple
T20413232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jumong |
E500641
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Soseono |
—
|
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: Soseono | Statement: [Jumong, spouse, Soseono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Soseono Context triple: [Jumong, spouse, Soseono]
-
A.
Soseono
chosen
Soseono was a prominent queen and political figure in ancient Korea, known as a key founder and supporter of the early Goguryeo and Baekje kingdoms.
-
B.
Sōri
Sōri was an early art name used by the renowned Japanese ukiyo-e master Katsushika Hokusai during the formative period of his printmaking career.
-
C.
Saiun
Saiun is the Allied reporting name for the Nakajima C6N, a fast and long-range Japanese carrier-based reconnaissance aircraft used during World War II.
-
D.
Sonezaki
Sonezaki is a well-known commercial and entertainment district in Osaka, Japan, noted for its bustling nightlife, restaurants, and traditional urban atmosphere.
-
E.
Seiyo
Seiyo is a city located in western Shikoku, Japan, known for its rural landscapes, terraced fields, and coastal scenery along the Uwa Sea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a417f208190be9bc11650ee0a87 |
completed | April 20, 2026, 7:10 p.m. |
Created at: April 16, 2026, 11:30 a.m.