Triple
T4998922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Togo Shrine (Tokyo) |
E112317
|
entity |
| Predicate | hasGroundsType |
P16808
|
FINISHED |
| Object | forested precinct |
—
|
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: forested precinct | Statement: [Togo Shrine (Tokyo), hasGroundsType, forested precinct]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGroundsType Context triple: [Togo Shrine (Tokyo), hasGroundsType, forested precinct]
-
A.
hasGrounds
Indicates that one entity possesses or includes a physical area of land or outdoor space associated with it.
-
B.
haveType
chosen
Indicates that an entity belongs to or is classified under a specified type or category.
-
C.
coversGrounds
Indicates that one entity extends over, occupies, or lies across the surface area of another entity (typically land or grounds).
-
D.
hasGroundState
Indicates that an entity possesses a lowest-energy, most stable state in its energy configuration.
-
E.
hasGrainType
Indicates that an entity is characterized by or associated with a specific type of grain.
- 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_69bd4432b32c81909f3b3c6bd10f0653 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7472a1dc8190942f568a81fdd961 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd714aee2481908fb0dd5fa2daf3a1 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:34 p.m.