Triple
T21541493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jingū |
E531499
|
entity |
| Predicate | mainSanctuaryMaterial |
P132141
|
FINISHED |
| Object | untreated Japanese cypress wood |
—
|
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: untreated Japanese cypress wood | Statement: [Jingū, mainSanctuaryMaterial, untreated Japanese cypress wood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainSanctuaryMaterial Context triple: [Jingū, mainSanctuaryMaterial, untreated Japanese cypress wood]
-
A.
hasSanctuary
Indicates that one entity provides or serves as a place of refuge, protection, or safe haven for another entity.
-
B.
sanctumContains
chosen
Indicates that one sanctum or sacred space physically or conceptually includes or holds another entity within its bounds.
-
C.
mainAltarMaterial
Indicates the material from which the main altar is made.
-
D.
hasRitualObjectMaterial
Indicates that a ritual object is made from, composed of, or primarily constructed using a specified material.
-
E.
involvesSanctuary
Indicates that an action, event, or relationship includes or takes place within a sanctuary or protected refuge.
- 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_69e0c45f17148190949c330ab9c27706 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d12b264819096f844b5833198aa |
completed | April 26, 2026, 11:17 p.m. |
| PD | Predicate disambiguation | batch_69e6320766308190ba5dca2f7c826aa4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:28 p.m.