Triple
T19054027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shimen District |
E466342
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Sanzhi District |
—
|
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: Sanzhi District | Statement: [Shimen District, borderedBy, Sanzhi District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanzhi District Context triple: [Shimen District, borderedBy, Sanzhi District]
-
A.
Sanzhi District
chosen
Sanzhi District is a rural coastal district in northern Taiwan known for its scenic landscapes, hot springs, and agricultural produce within New Taipei City.
-
B.
Zhanqian District
Zhanqian District is an urban administrative district under the jurisdiction of Yingkou City in Liaoning Province, China.
-
C.
Xialu District
Xialu District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China.
-
D.
Hecheng District
Hecheng District is the central urban district and administrative seat of Huaihua in Hunan Province, China.
-
E.
Zhanyi District
Zhanyi District is an administrative district under the jurisdiction of Qujing City in Yunnan Province, China, known for its role in regional agriculture and transportation.
- 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_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5dc049f64819093ae9fda26a49bd2 |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 10, 2026, 12:03 p.m.