Triple
T16687277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yuanmingyuan Park station |
E405494
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Beijing |
E2312
|
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: Beijing | Statement: [Yuanmingyuan Park station, cityServed, Beijing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beijing Context triple: [Yuanmingyuan Park station, cityServed, Beijing]
-
A.
Beijing
chosen
Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
-
B.
Pekin
Pekin is a small hamlet in Niagara County, New York, known historically as a stop on the Underground Railroad.
-
C.
Tiāntán
Tiāntán is the Chinese pinyin name for the Temple of Heaven, a historic imperial religious complex in Beijing where Ming and Qing dynasty emperors performed annual ceremonies to pray for good harvests.
-
D.
Shanghai
Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
-
E.
Shanghai
Shanghai is an unincorporated community located in Berkeley County, West Virginia, United States.
- 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_69d8838c28748190b3f5967c743940ab |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37ea63b7081908a055036172f9683 |
completed | April 18, 2026, 12:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a275910819086b49ce741045ecf |
completed | May 10, 2026, 1:37 p.m. |
Created at: April 10, 2026, 5:19 a.m.