Triple
T25209401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scarlett Amber Perkins |
E631639
|
entity |
| Predicate | hasRelationshipTypeWith Nobody Owens |
P179177
|
FINISHED |
| Object | friendship |
—
|
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: friendship | Statement: [Scarlett Amber Perkins, hasRelationshipTypeWith Nobody Owens, friendship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWith Nobody Owens Context triple: [Scarlett Amber Perkins, hasRelationshipTypeWith Nobody Owens, friendship]
-
A.
hasRelationshipTypeWith Vince Tyler
Indicates that an entity is connected to Vince Tyler by a specific, characterized type of relationship.
-
B.
hasRelationshipTypeWith Frank Drebin
Indicates that there exists a specific type of relationship between an entity and Frank Drebin.
-
C.
hasRelationshipTypeWithRocky
Indicates that an entity has a specific type of relationship or connection with the entity named Rocky.
-
D.
hasRelationshipTypeWithOmar
Indicates that an entity stands in a specified type of interpersonal or associative relationship with Omar.
-
E.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
- 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_69e75a8d1aa48190a4320acd3654762c |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f7201e241c819092d56a7bb99dc94d |
completed | May 3, 2026, 10:14 a.m. |
| PD | Predicate disambiguation | batch_69f71cc405c08190863565609a4c8499 |
completed | May 3, 2026, 10 a.m. |
| PDg | Predicate description generation | batch_69f71f8df5d48190944fbfbd9d573868 |
completed | May 3, 2026, 10:12 a.m. |
Created at: April 21, 2026, 12:58 p.m.