Triple
T32063842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warby Parker |
E818819
|
entity |
| Predicate | socialImpactModel |
P4312
|
FINISHED |
| Object | donates glasses through partners |
—
|
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: donates glasses through partners | Statement: [Warby Parker, socialImpactModel, donates glasses through partners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: socialImpactModel Context triple: [Warby Parker, socialImpactModel, donates glasses through partners]
-
A.
socialImpact
chosen
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
B.
exportImpact
Indicates the effect or consequences that an entity’s exports have on another entity, system, or context.
-
C.
recognizesImpactOn
Indicates that one entity acknowledges or understands the effect or consequences it has on another entity or situation.
-
D.
socialConcern
Indicates a relationship where an entity is concerned about, attentive to, or actively engaged with social issues, problems, or well-being.
-
E.
socialEstate
Indicates a relationship where an entity belongs to or is classified within a particular social class, rank, or estate in a societal hierarchy.
- 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_69f348fecc088190af1470afe5a969f0 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: May 1, 2026, 12:22 a.m.