Triple
T6047606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Levin |
E134705
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Levi |
E11399
|
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: Levi | Statement: [Levin, hasVariant, Levi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Levi Context triple: [Levin, hasVariant, Levi]
-
A.
Levi
chosen
Levi is the surname of Primo Levi, the renowned Italian Jewish chemist and writer best known for his memoirs about surviving the Auschwitz concentration camp.
-
B.
Levi
Levi is a biblical patriarch, one of the twelve sons of Jacob and ancestor of the Israelite tribe of Levi, traditionally associated with priestly duties.
-
C.
Levi
Levi is a popular ski resort and tourist destination in Finnish Lapland, known for its extensive slopes, winter sports, and vibrant holiday village.
-
D.
Levi Todd
Levi Todd was an early American pioneer and political figure associated with the prominent Todd family, known for his role in the settlement and governance of Kentucky.
-
E.
Pepe Jeans
Pepe Jeans is a British denim and casualwear fashion brand known for its trendy jeans and youthful, urban style.
- 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056e70fd48190b4554e9a516a1c88 |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c113a2b65c8190a1891e790cb9a5de |
completed | March 23, 2026, 10:19 a.m. |
Created at: March 22, 2026, 4:09 p.m.