Triple
T26729541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyo Racecourse |
E673927
|
entity |
| Predicate | hasOuterTurfCourse |
P166626
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Tokyo Racecourse, hasOuterTurfCourse, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOuterTurfCourse Context triple: [Tokyo Racecourse, hasOuterTurfCourse, yes]
-
A.
hasInnerTurfCourse
Indicates that something (typically a race track or stadium) possesses an inner turf course located within its main track or field.
-
B.
hasGolfCourse
Indicates that one entity possesses, contains, or includes a golf course as part of its facilities or attributes.
-
C.
hasParEastCourse
Indicates that an entity has a related course located to its east that is considered parallel (par) to it in layout or alignment.
-
D.
hasTennisCourt
Indicates that one entity possesses, includes, or provides access to a tennis court as part of its facilities or attributes.
-
E.
hasLakeOnCourse
Indicates that a course (such as a route or path) includes at least one lake situated along its way or within its boundaries.
- 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_69eecda57ab481909424e98f2835e7d8 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f662a29b3881909957a7e3b986653c |
completed | May 2, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69f661b47d088190934f63884a203261 |
completed | May 2, 2026, 8:42 p.m. |
Created at: April 27, 2026, 3:44 a.m.