Triple
T17445969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 首都体育馆 |
E424784
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | 北京市 |
—
|
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: 北京市 | Statement: [首都体育馆, locatedIn, 北京市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 北京市 Context triple: [首都体育馆, locatedIn, 北京市]
-
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.
Tân An
Tân An is a city in southern Vietnam that serves as an administrative, economic, and cultural hub in the Mekong Delta region.
-
C.
Haidian District
Haidian District is a major urban district in northwest Beijing known for its universities, technology hubs, and historic imperial gardens.
-
D.
Tianjin
Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
-
E.
Pekin
Pekin is a small hamlet in Niagara County, New York, known historically as a stop on the Underground Railroad.
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e44ffafec48190b25980318cea353f |
completed | April 19, 2026, 3:46 a.m. |
Created at: April 10, 2026, 5:47 a.m.