Triple
T9872854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sid Phillips |
E239999
|
entity |
| Predicate | toyTreatment |
P90989
|
FINISHED |
| Object | views toys as inanimate objects |
—
|
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: views toys as inanimate objects | Statement: [Sid Phillips, toyTreatment, views toys as inanimate objects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: toyTreatment Context triple: [Sid Phillips, toyTreatment, views toys as inanimate objects]
-
A.
toyOwner
Indicates that one entity is the owner or possessor of a particular toy belonging to another entity.
-
B.
primaryToy
Indicates that one entity is the main or most frequently used toy associated with another entity.
-
C.
pet
Indicates that one entity keeps another animal for companionship or pleasure, typically providing care and shelter.
-
D.
toyLine
Indicates that one entity is part of, or associated with, a particular toy product line or series.
-
E.
companionAnimals
Indicates a relationship where one entity keeps or cares for another entity as a pet or companion animal.
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3f754008190abe3fe034b42908e |
completed | April 2, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
| PDg | Predicate description generation | batch_69cd3581a9688190a00cef4c3eebb0ae |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 8:37 p.m.