Triple
T2073571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mengjiang |
E44869
|
entity |
| Predicate | currency |
P245
|
FINISHED |
| Object | Mengjiang yuan |
E44869
|
NE 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: Mengjiang yuan | Statement: [Mengjiang, currency, Mengjiang yuan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mengjiang yuan Context triple: [Mengjiang, currency, Mengjiang yuan]
-
A.
Mengjiang
chosen
Mengjiang was a Japanese puppet state established in Inner Mongolia during the Second Sino-Japanese War and World War II.
-
B.
Changling
Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
-
C.
Yuxiang
Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
-
D.
Chuping
Chuping is a town in the Malaysian state of Perlis, known for its extensive sugarcane plantations and hot climate.
-
E.
Xiaochang
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a88916c2b48190a5ca2e9b12cad3ed |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba101c008190840763d2f28fa8d7 |
completed | March 7, 2026, 5:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae272ee27c8190a4bb4690961dccf6 |
completed | March 9, 2026, 1:49 a.m. |
Created at: March 4, 2026, 7:41 p.m.