Triple
T2567010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arashiyama Bamboo Grove |
E57374
|
entity |
| Predicate | typicalVisitActivity |
P1164
|
FINISHED |
| Object | walking |
—
|
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: walking | Statement: [Arashiyama Bamboo Grove, typicalVisitActivity, walking]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVisitActivity Context triple: [Arashiyama Bamboo Grove, typicalVisitActivity, walking]
-
A.
typicalActivity
chosen
Indicates that an entity is commonly or characteristically engaged in a particular activity.
-
B.
canVisit
Indicates that one entity is permitted or able to go to or access another entity or location.
-
C.
typicalAppointment
Indicates that an appointment represents a standard, usual, or commonly occurring scheduling arrangement between entities.
-
D.
activityType
Indicates the specific kind or category of action or event that an entity is engaged in or associated with.
-
E.
typicalSchedule
Indicates the usual or standard timing and sequence of activities or events associated with an entity.
- 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_69ab4a4ef9008190a0e6d4422b9418b7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3602ed08190aad0f9c7ac577eb0 |
completed | March 7, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69abd0cc8d308190ae7aa32b8f5ae2e5 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:48 p.m.