Triple
T11237708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EVA |
E265985
|
entity |
| Predicate | baseCity |
P6317
|
FINISHED |
| Object | Taoyuan |
E159989
|
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: Taoyuan | Statement: [EVA, baseCity, Taoyuan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taoyuan Context triple: [EVA, baseCity, Taoyuan]
-
A.
Taoyuan City
chosen
Taoyuan City is a major municipality in northwestern Taiwan known for its rapidly growing urban areas, industrial zones, and proximity to Taiwan Taoyuan International Airport.
-
B.
Taoyuan District
Taoyuan District is the central urban and administrative hub of Taoyuan City in northwestern Taiwan, known for its dense population, commercial activity, and transportation links.
-
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.
Taoyuan Xiang
Taoyuan Xiang is a regional variety of the Xiang Chinese language spoken primarily in the Taoyuan area of Hunan Province, China.
-
E.
Xinyi
Xinyi is a county-level city administered by Xuzhou in Jiangsu Province, eastern China.
- 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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e918375081908c2a7ccb50cbf331 |
completed | April 9, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4f3eca6bc8190bc0640353a505ad5 |
completed | April 19, 2026, 3:25 p.m. |
Created at: April 8, 2026, 9:30 p.m.