Triple
T5943136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E. W. Scripps Company |
E132214
|
entity |
| Predicate | formerBusiness |
P5635
|
FINISHED |
| Object | newspaper publishing |
—
|
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: newspaper publishing | Statement: [E. W. Scripps Company, formerBusiness, newspaper publishing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerBusiness Context triple: [E. W. Scripps Company, formerBusiness, newspaper publishing]
-
A.
formerMarket
Indicates that an entity previously functioned as a market but no longer serves that role.
-
B.
formerBusinessModel
chosen
Indicates that an entity previously operated under a particular business model, but no longer does so.
-
C.
formerProduct
Indicates that an entity was previously a product of another entity but is no longer in that role or status.
-
D.
formerEmployer
Indicates that one entity previously employed the other but no longer does so.
-
E.
formerCEO
Indicates that one entity previously held, but no longer holds, the position of chief executive officer of the other entity.
- 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_69c00869d3308190af89b2453e0f7546 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03ee10b308190afe38b904ae7c5f7 |
completed | March 22, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69c0335806788190b6488ca8b73f7a63 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:01 p.m.