Triple
T6525474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3M |
E151290
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object |
Nexcare
Nexcare is a 3M healthcare brand known for its bandages, medical tapes, and other first-aid and wound care products.
|
E602883
|
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: Nexcare | Statement: [3M, brand, Nexcare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nexcare Context triple: [3M, brand, Nexcare]
-
A.
Cicaplast
Cicaplast is La Roche-Posay’s skin-repair line formulated to soothe, protect, and support the healing of sensitive or damaged skin.
-
B.
Blistex
Blistex is an American company best known for producing lip care and other medicated skin care products.
-
C.
Scholl
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.
-
D.
Opekta
Opekta was a German-Dutch company that produced and sold pectin-based gelling agents for making jam, notably managed in its Amsterdam branch by Anne Frank’s father, Otto Frank.
-
E.
Vaseline
Vaseline is a well-known skincare brand best recognized for its petroleum jelly products used to moisturize, protect, and heal dry or damaged skin.
- 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: Nexcare Triple: [3M, brand, Nexcare]
Generated description
Nexcare is a 3M healthcare brand known for its bandages, medical tapes, and other first-aid and wound care products.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nexcare Target entity description: Nexcare is a 3M healthcare brand known for its bandages, medical tapes, and other first-aid and wound care products.
-
A.
Cicaplast
Cicaplast is La Roche-Posay’s skin-repair line formulated to soothe, protect, and support the healing of sensitive or damaged skin.
-
B.
Blistex
Blistex is an American company best known for producing lip care and other medicated skin care products.
-
C.
Scholl
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.
-
D.
Opekta
Opekta was a German-Dutch company that produced and sold pectin-based gelling agents for making jam, notably managed in its Amsterdam branch by Anne Frank’s father, Otto Frank.
-
E.
Vaseline
Vaseline is a well-known skincare brand best recognized for its petroleum jelly products used to moisturize, protect, and heal dry or damaged skin.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ada77b448190b6e0e07494fb4dd3 |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d5271b7c8190a602f6ec72efe04c |
completed | March 27, 2026, 7:06 p.m. |
| NEDg | Description generation | batch_69c6d622831c8190b09b8539e36afb7c |
completed | March 27, 2026, 7:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6d6b00ebc8190b893b3e209bf07c0 |
completed | March 27, 2026, 7:12 p.m. |
Created at: March 27, 2026, 1:45 p.m.