Triple
T36062572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Kiritsubo |
E1043126
|
entity |
| Predicate | burialOrMemorial |
P184532
|
FINISHED |
| Object | mourned deeply by the emperor |
—
|
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: mourned deeply by the emperor | Statement: [Lady Kiritsubo, burialOrMemorial, mourned deeply by the emperor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: burialOrMemorial Context triple: [Lady Kiritsubo, burialOrMemorial, mourned deeply by the emperor]
-
A.
burialDetail
Indicates details about how, where, and under what circumstances an entity is buried.
-
B.
burialBy
Indicates that one entity is responsible for burying or interring another entity.
-
C.
hasGraveOrMemorialOf
Indicates that a location or object serves as the grave or memorial site dedicated to a particular person or entity.
-
D.
burialText
Indicates that a text is inscribed on, associated with, or used in connection with a burial or funerary context.
-
E.
burialPlace
Indicates the location where a person or entity is buried.
- 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_69f76e2f09448190b0486d5ecad5e243 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:08 p.m.