Triple
T8414593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hsinchu |
E198701
|
entity |
| Predicate | hasScienceParkEstablished |
P32590
|
FINISHED |
| Object | 1980s (Hsinchu Science Park era) |
—
|
LITERAL 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: 1980s (Hsinchu Science Park era) | Statement: [Hsinchu, hasScienceParkEstablished, 1980s (Hsinchu Science Park era)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScienceParkEstablished Context triple: [Hsinchu, hasScienceParkEstablished, 1980s (Hsinchu Science Park era)]
-
A.
hasSciencePark
chosen
Indicates that one entity possesses, hosts, or includes a science park associated with it.
-
B.
hasScienceCenter
Indicates that an entity possesses, hosts, or includes a science center as one of its facilities or components.
-
C.
isScienceCity
Indicates that a city is recognized or designated as a center for scientific research, education, or technological innovation.
-
D.
hasIndustrialPark
Indicates that a location or entity possesses or contains an industrial park within its area or jurisdiction.
-
E.
hasBusinessPark
Indicates that one entity possesses, contains, or is associated with a business park as part of its facilities or properties.
- F. None of above.
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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e328cc8190b3b038005d0bb66f |
completed | March 31, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69cb70d70ea081909c3dc1bd2ec14f85 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:06 p.m.