Triple
T23243782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuan Shikai |
E581528
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Shikai |
—
|
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: Shikai | Statement: [Yuan Shikai, givenName, Shikai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shikai Context triple: [Yuan Shikai, givenName, Shikai]
-
A.
Shikai
chosen
Shikai is the given name of Yuan Shikai, the Chinese military and political leader who became the first president of the Republic of China.
-
B.
Tateishi
Tateishi is a neighborhood in Tokyo known for its traditional shitamachi atmosphere, narrow shopping streets, and old-style bars and eateries.
-
C.
Shinjo
Shinjo is a notable figure associated with the Kegon school of Japanese Buddhism, recognized for contributions to its teachings or development.
-
D.
Norikura
Norikura is a ski resort area in Japan’s Hakuba Valley, known for its scenic alpine terrain and winter sports opportunities.
-
E.
Asakura
Asakura is a city in Fukuoka Prefecture, Japan, known for its rural landscapes, historic sites, and agricultural products such as fruits and vegetables.
- 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_69e24606b17c81908aba1a4911c8a8ba |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f192efd44c8190b179b4d1cb71efa5 |
completed | April 29, 2026, 5:11 a.m. |
Created at: April 17, 2026, 4:10 p.m.