Triple
T21541496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jingū |
E531499
|
entity |
| Predicate | rebuildingCycleYears |
P144165
|
FINISHED |
| Object | 20 |
—
|
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: 20 | Statement: [Jingū, rebuildingCycleYears, 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rebuildingCycleYears Context triple: [Jingū, rebuildingCycleYears, 20]
-
A.
rebuildPeriod
Indicates the time span during which something is reconstructed or restored after damage, failure, or decommissioning.
-
B.
rebuiltBetween
Indicates that an entity was reconstructed or rebuilt during a time interval bounded by two specified temporal points.
-
C.
reclamationPeriod
Indicates the time span during which something that was previously taken, lost, or deactivated can be recovered or restored.
-
D.
reconstructionYear
Indicates the year in which something was rebuilt, restored, or reconstructed after damage, alteration, or destruction.
-
E.
rebuiltMultipleTimes
Indicates that the same entity has been reconstructed or restored on more than one separate occasion.
- 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_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. |
| PDg | Predicate description generation | batch_69e633bf34c481909925d8dc1a633a65 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 16, 2026, 6:28 p.m.