Triple
T17180078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wang Da-hong |
E416957
|
entity |
| Predicate | regionOfInfluence |
P2828
|
FINISHED |
| Object | Taipei |
—
|
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: Taipei | Statement: [Wang Da-hong, regionOfInfluence, Taipei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taipei Context triple: [Wang Da-hong, regionOfInfluence, Taipei]
-
A.
Taipei, Taiwan
chosen
Taipei, Taiwan is the capital and largest city of Taiwan, known as a major political, economic, and cultural center in East Asia.
-
B.
Tainan
Tainan is a historic city in southern Taiwan known for its well-preserved temples, traditional culture, and status as the island’s former capital.
-
C.
Taichung
Taichung is a major city in central Taiwan known for its cultural attractions, mild climate, and role as an important economic and transportation hub.
-
D.
Kaohsiung
Kaohsiung is a major port city in southern Taiwan known for its heavy industry, modern harborfront, and growing cultural and arts scene.
-
E.
Keelung
Keelung is a major port city in northeastern Taiwan known for its busy harbor, seafood markets, and coastal scenery.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc10afb48190a71f4a46f0280a14 |
completed | April 18, 2026, 9:48 p.m. |
Created at: April 10, 2026, 5:37 a.m.