Triple
T704390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgia-Pacific |
E14067
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object |
Angel Soft
Angel Soft is a popular American toilet paper brand known for its balance of softness, strength, and affordability.
|
E85150
|
NE FINISHED |
How this triple was built (4 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: Angel Soft | Statement: [Georgia-Pacific, brand, Angel Soft]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angel Soft Context triple: [Georgia-Pacific, brand, Angel Soft]
-
A.
Edelweiss
"Edelweiss" is a gentle, nostalgic song from the musical *The Sound of Music*, widely recognized as one of Richard Rodgers and Oscar Hammerstein II’s most beloved compositions.
-
B.
Dulce Domum
"Dulce Domum" is a nostalgic and emotionally rich chapter in Kenneth Grahame's classic children's novel *The Wind in the Willows*, focusing on Mole's return to his long-neglected home.
-
C.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
-
D.
Doux
Doux is the sweetest style of Champagne, characterized by a high sugar content that gives it a rich, dessert-like taste.
-
E.
Madruga
Madruga is a municipality in western Cuba known for its rural character and location within the historical region surrounding Havana.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Angel Soft Triple: [Georgia-Pacific, brand, Angel Soft]
Generated description
Angel Soft is a popular American toilet paper brand known for its balance of softness, strength, and affordability.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Angel Soft Target entity description: Angel Soft is a popular American toilet paper brand known for its balance of softness, strength, and affordability.
-
A.
Edelweiss
"Edelweiss" is a gentle, nostalgic song from the musical *The Sound of Music*, widely recognized as one of Richard Rodgers and Oscar Hammerstein II’s most beloved compositions.
-
B.
Dulce Domum
"Dulce Domum" is a nostalgic and emotionally rich chapter in Kenneth Grahame's classic children's novel *The Wind in the Willows*, focusing on Mole's return to his long-neglected home.
-
C.
Annabella
Annabella was a French film actress of the 1930s and 1940s, known for her work in both European and Hollywood cinema.
-
D.
Doux
Doux is the sweetest style of Champagne, characterized by a high sugar content that gives it a rich, dessert-like taste.
-
E.
Madruga
Madruga is a municipality in western Cuba known for its rural character and location within the historical region surrounding Havana.
- F. None of above. chosen
Provenance (5 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a533fa788190bba0f55655469c46 |
completed | March 1, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5dcae2ef88190a9ea1604429f048a |
completed | March 2, 2026, 6:53 p.m. |
| NEDg | Description generation | batch_69a5df14e1788190bb2f2cc87cadcb40 |
completed | March 2, 2026, 7:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5ff5dd4808190bb8ae25fbdca0075 |
completed | March 2, 2026, 9:21 p.m. |
Created at: March 1, 2026, 7:36 p.m.