Triple
T3468113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lavazza |
E73185
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Lavazza |
E73185
|
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: Lavazza | Statement: [Lavazza, brand, Lavazza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lavazza Context triple: [Lavazza, brand, Lavazza]
-
A.
Lavazza
chosen
Lavazza is a major Italian coffee company renowned worldwide for its espresso blends and coffee products.
-
B.
Federico Caffè
Federico Caffè was an influential Italian economist and academic known for his work on welfare economics, Keynesian theory, and social justice in economic policy.
-
C.
Nespresso
Nespresso is a premium coffee brand known for its single-serve espresso machines and coffee capsules marketed worldwide.
-
D.
Faema
Faema was a prominent professional Italian cycling team of the 1950s and 1960s, best known for sponsoring and supporting legendary riders such as Eddy Merckx.
-
E.
Facinelli
Facinelli is an Italian-origin surname best known through American actor Peter Facinelli, recognized for roles in the Twilight film series and various television shows.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb11ec5881908347bf92883a25ee |
completed | March 8, 2026, 6:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3680763608190acdd146dc7c0b239 |
completed | March 13, 2026, 1:27 a.m. |
Created at: March 8, 2026, 3:17 p.m.