Triple
T37971407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Digitalscape |
E947295
|
entity |
| Predicate | targetMarketOfWork |
P481
|
FINISHED |
| Object | children and young audiences |
—
|
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: children and young audiences | Statement: [Digitalscape, targetMarketOfWork, children and young audiences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetMarketOfWork Context triple: [Digitalscape, targetMarketOfWork, children and young audiences]
-
A.
targetMarket
chosen
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
-
B.
targetWork
Indicates that one entity is the specific work (e.g., document, artwork, or project) that another entity is directed at, refers to, or is primarily concerned with.
-
C.
targetAudienceOfOriginWork
Indicates the intended audience or demographic group for which the original work was created.
-
D.
targetOfficeSought
Indicates the specific public or organizational office or position that an individual is seeking or running for.
-
E.
employerFocus
Indicates that an employer directs particular attention, resources, or priority toward a specific subject, group, or area.
- 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_69f76ef7db908190bba6086673a32300 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ff4fc6077c8190b8fd9b43fcfde986 |
completed | May 9, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69ff4e61fb648190a72f7918961ece9c |
completed | May 9, 2026, 3:10 p.m. |
Created at: May 3, 2026, 4:20 p.m.