Triple
T35361393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juli Baker |
E1021493
|
entity |
| Predicate | relationshipTypeWithBryceLoski |
P206951
|
FINISHED |
| Object | childhood crush |
—
|
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 crush | Statement: [Juli Baker, relationshipTypeWithBryceLoski, childhood crush]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithBryceLoski Context triple: [Juli Baker, relationshipTypeWithBryceLoski, childhood crush]
-
A.
relationshipTypeWith Brian Flanagan
Indicates the specific nature or category of relationship that an entity has with Brian Flanagan.
-
B.
relationshipTypeWithJoshSrebnick
Indicates the specific nature or category of the relationship that an entity has with Josh Srebnick.
-
C.
hasRelationshipTypeWith Vince Tyler
Indicates that an entity is connected to Vince Tyler by a specific, characterized type of relationship.
-
D.
relationshipTypeWithLuciousLyon
Indicates the specific nature or category of relationship that an entity has with Lucious Lyon.
-
E.
relationshipTypeWithTracyFishko
Indicates the specific nature or category of relationship that an entity has with Tracy Fishko.
- 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_69f76def44c881908a20e8008572eb44 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.