Triple
T1629492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canton Tower |
E35224
|
entity |
| Predicate | locatedInDistrict |
P40
|
FINISHED |
| Object | Haizhu District |
E194167
|
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: Haizhu District | Statement: [Canton Tower, locatedInDistrict, Haizhu District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haizhu District Context triple: [Canton Tower, locatedInDistrict, Haizhu District]
-
A.
Haizhu District
chosen
Haizhu District is a central urban district of Guangzhou, China, known for its mix of residential areas, commercial centers, and cultural sites along the Pearl River.
-
B.
Yuhua District
Yuhua District is an urban administrative district of Changsha, the capital city of Hunan Province in south-central China.
-
C.
Xicheng District
Xicheng District is a central urban district of Beijing, China, known for its historic sites, government institutions, and cultural landmarks.
-
D.
Pingshan District
Pingshan District is an administrative district in the eastern part of Shenzhen, China, known for its emerging high-tech industries and rapid urban development.
-
E.
Qingshan District
Qingshan District is an urban district of Wuhan in Hubei Province, China, known for its heavy industry and riverside location along the Yangtze River.
- 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb3a9b634819086b44f1574e97dcb |
completed | March 7, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0c3111081909bb8e503e77351cf |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 4, 2026, 7:28 p.m.