Triple
T36062571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady Kiritsubo |
E1043126
|
entity |
| Predicate | causeOfResentment |
P193440
|
FINISHED |
| Object | emperor’s special affection |
—
|
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: emperor’s special affection | Statement: [Lady Kiritsubo, causeOfResentment, emperor’s special affection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfResentment Context triple: [Lady Kiritsubo, causeOfResentment, emperor’s special affection]
-
A.
causeOfVengeance
Indicates a relationship where one entity is the reason or trigger for another entity’s desire or act of vengeance.
-
B.
causeOf
Indicates that one entity brings about, produces, or is responsible for the occurrence or existence of another entity or event.
-
C.
harborsResentmentToward
Indicates that one entity holds ongoing bitterness, anger, or ill will directed at another entity.
-
D.
hasCauseOfConflict
Indicates a relationship where one entity is the source or reason for a conflict involving another entity.
-
E.
causeOfGuilt
Indicates that one entity is the reason or source of another entity’s feeling or state of guilt.
- 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_69fd474b7e788190a9bb9b542d878f60 |
completed | May 8, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69fd46d8b2f0819099d92d72c902f60e |
completed | May 8, 2026, 2:13 a.m. |
| PDg | Predicate description generation | batch_69fd474a71648190b6b6ae4991db81b1 |
completed | May 8, 2026, 2:15 a.m. |
Created at: May 3, 2026, 4:08 p.m.