Triple
T30347740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ai Hayasaka |
E771909
|
entity |
| Predicate | relationshipTypeWith Kaguya Shinomiya |
P38921
|
FINISHED |
| Object | childhood acquaintance |
—
|
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: childhood acquaintance | Statement: [Ai Hayasaka, relationshipTypeWith Kaguya Shinomiya, childhood acquaintance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWith Kaguya Shinomiya Context triple: [Ai Hayasaka, relationshipTypeWith Kaguya Shinomiya, childhood acquaintance]
-
A.
relationshipToYukina
Indicates a specified type of personal or social connection that an entity has with Yukina.
-
B.
relationshipTypeWithKatnissEverdeen
Indicates the type or nature of the relationship an entity has with Katniss Everdeen.
-
C.
haveRelationshipWith
Indicates that one entity is in some form of defined relationship or association with another entity.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
- 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_69f2248b9a208190bc3e6804acd5afd6 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a0245e917748190bf0a65db538aa66f |
completed | May 11, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_6a023f7cf7148190af5c2ea501511145 |
completed | May 11, 2026, 8:43 p.m. |
Created at: April 29, 2026, 7:56 p.m.