Triple
T6076145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Scholl |
E135404
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Scholl |
E561844
|
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: Scholl | Statement: [Hans Scholl, familyName, Scholl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scholl Context triple: [Hans Scholl, familyName, Scholl]
-
A.
Scholl
chosen
Scholl is the surname of Sophie Scholl, the German student and anti-Nazi resistance member known for her role in the White Rose movement during World War II.
-
B.
Lifebuoy
Lifebuoy is a long-established global soap and hygiene brand known for its antibacterial products and health-focused marketing.
-
C.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
D.
Suavitel
Suavitel is a popular fabric softener brand known for its long-lasting fragrances and softening properties, marketed primarily in Latin American and U.S. Hispanic households.
-
E.
Neutrogena
Neutrogena is a widely recognized skincare and cosmetics brand known for its dermatologist-recommended products, including facial cleansers, moisturizers, sunscreens, and acne treatments.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0575ec63081908a868a41855acf73 |
completed | March 22, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d43f7908190845c2337cd243a3c |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:11 p.m.