Triple
T4068942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pacific Blue |
E86596
|
entity |
| Predicate | brandOf |
P1500
|
FINISHED |
| Object | Georgia-Pacific |
E14067
|
NE 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: Georgia-Pacific | Statement: [Pacific Blue, brandOf, Georgia-Pacific]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Georgia-Pacific Context triple: [Pacific Blue, brandOf, Georgia-Pacific]
-
A.
Georgia-Pacific
chosen
Georgia-Pacific is a major American pulp and paper company known for producing tissue, packaging, building products, and related chemicals.
-
B.
Weyerhaeuser Company
Weyerhaeuser Company is a major American timberland and forest products company, historically one of the world’s largest private owners of softwood timber.
-
C.
International Paper
International Paper is a leading global producer of renewable fiber-based packaging, pulp, and paper products.
-
D.
UPM
UPM is the Polytechnic University of Madrid, a leading Spanish public university specializing in engineering, architecture, and technology.
-
E.
Northern Lumber Company
Northern Lumber Company was a historic American timber and lumber firm connected to prominent lumber baron Frederick Weyerhaeuser and the broader Upper Midwest logging industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69aed93ebe448190a1f1686e28740ac9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefbf8f33c8190a6afca1830f35485 |
completed | March 9, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b56b58731c81908058702880120335 |
completed | March 14, 2026, 2:06 p.m. |
Created at: March 9, 2026, 3:38 p.m.