Triple
T4214738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsukuba |
E94187
|
entity |
| Predicate | isScienceCity |
P54790
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Tsukuba, isScienceCity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isScienceCity Context triple: [Tsukuba, isScienceCity, true]
-
A.
hasScienceCenter
Indicates that an entity possesses, hosts, or includes a science center as one of its facilities or components.
-
B.
hasSciencePark
Indicates that one entity possesses, hosts, or includes a science park associated with it.
-
C.
isScientificCenterFor
Indicates that one entity serves as a primary location or institution dedicated to conducting, coordinating, or supporting scientific research, education, or activities related to another entity.
-
D.
isPlannedCity
Indicates that a city has been deliberately designed and constructed according to a pre-established urban plan rather than developing organically over time.
-
E.
museumCity
Indicates the city in which a given museum is located.
- F. None of above. chosen
Provenance (4 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_69b3451997e08190851db4a9a588837d |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e098da881909a0cc339cc186627 |
completed | March 12, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69b347efd9b08190bb50f82e4e7fe06d |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e04ef1c81908bb34ae1cbfab1e6 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:04 p.m.