Triple
T11731103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NJOY |
E278897
|
entity |
| Predicate | competitor |
P1375
|
FINISHED |
| Object | Juul |
E278895
|
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: Juul | Statement: [NJOY, competitor, Juul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juul Context triple: [NJOY, competitor, Juul]
-
A.
Juul
chosen
Juul is a popular but controversial electronic cigarette brand known for its sleek USB-like design and significant role in the rise of youth vaping.
-
B.
IQOS
IQOS is a heated tobacco product developed by Philip Morris International that offers an alternative to traditional cigarette smoking by warming tobacco instead of burning it.
-
C.
NJOY
NJOY is an American e-cigarette and vaping brand known for producing electronic nicotine delivery systems as an alternative to traditional cigarettes.
-
D.
Vuse
Vuse is an electronic cigarette and vaping product brand owned by British American Tobacco, known for its range of nicotine e-liquids and devices.
-
E.
ZYN
ZYN is a popular brand of nicotine pouches known for offering tobacco-free, spit-free oral nicotine products in various flavors and strengths.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4d94de08190a7184cf26d8cb94e |
completed | April 10, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f09004c5908190bd6d7a29b266318b |
completed | April 28, 2026, 10:46 a.m. |
Created at: April 8, 2026, 9:41 p.m.