Triple
T18710575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerry Hickfang |
E457497
|
entity |
| Predicate | hasPetAlignmentContrast |
P123206
|
FINISHED |
| Object | Bosco as good conscience |
—
|
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: Bosco as good conscience | Statement: [Jerry Hickfang, hasPetAlignmentContrast, Bosco as good conscience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPetAlignmentContrast Context triple: [Jerry Hickfang, hasPetAlignmentContrast, Bosco as good conscience]
-
A.
providesContrastWith
chosen
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
B.
hasDensityContrast
Indicates that one entity differs from another in material density, highlighting a contrast in how compact or dense they are.
-
C.
achievesContrast
Indicates that one entity creates or enhances a visual or conceptual difference relative to another entity.
-
D.
hasPetTypeFocus
Indicates that an entity’s primary focus or concern is on a specific type or category of pet.
-
E.
hasAnimal
Indicates that one entity possesses, keeps, or is associated with an animal.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5671b34508190b6180f7d6ad50a58 |
completed | April 19, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69e478e0889c8190a118d67b200ce8ef |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:50 a.m.