Triple
T17851990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yizhuang |
E445829
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | 亦庄 |
—
|
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: 亦庄 | Statement: [Yizhuang, hasName, 亦庄]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 亦庄 Context triple: [Yizhuang, hasName, 亦庄]
-
A.
Yizhuang
chosen
Yizhuang is a rapidly developing suburban area in southeastern Beijing known for its economic and technological development zone and growing residential communities.
-
B.
Daxing District
Daxing District is a rapidly developing suburban district in southern Beijing, China, known for hosting the major Beijing Daxing International Airport and large-scale urban expansion.
-
C.
Shunyi District
Shunyi District is a suburban district of Beijing known for hosting Beijing Capital International Airport and a mix of residential, industrial, and international community areas.
-
D.
石景山区
石景山区 is an urban district in western Beijing, China, known for its industrial heritage, mountainous scenery, and attractions such as Shijingshan Amusement Park.
-
E.
西城区
西城区是中国北京市中心的一个重要城区，以其丰富的历史文化遗产和众多政府机关、金融机构的集中分布而著称。
- 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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48fff6c288190a2b5e60b66c03ddc |
completed | April 19, 2026, 8:19 a.m. |
Created at: April 10, 2026, 10:17 a.m.