Triple
T694174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | World of Coca-Cola |
E13859
|
entity |
| Predicate | dedicatedTo |
P500
|
FINISHED |
| Object | Coca-Cola |
E14062
|
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: Coca-Cola | Statement: [World of Coca-Cola, dedicatedTo, Coca-Cola]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coca-Cola Context triple: [World of Coca-Cola, dedicatedTo, Coca-Cola]
-
A.
The Coca-Cola Company
chosen
The Coca-Cola Company is a multinational beverage corporation best known as the producer of the iconic soft drink Coca-Cola and a wide portfolio of nonalcoholic beverages sold worldwide.
-
B.
Pepsi
Pepsi is a globally recognized carbonated soft drink brand produced by PepsiCo and known as one of the main competitors to Coca-Cola.
-
C.
P-Cola
P-Cola is a common shorthand nickname for the city of Pensacola in the Florida Panhandle.
-
D.
Nestlé
Nestlé is a Swiss multinational food and beverage conglomerate and one of the world’s largest consumer goods companies.
-
E.
Heinz
Heinz is the German given name of Henry Alfred Kissinger, the influential American diplomat and former U.S. Secretary of State.
- 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c3f39c8190a3014df428817492 |
completed | March 1, 2026, 8:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a64a53c068819089e66347da55710b |
completed | March 3, 2026, 2:41 a.m. |
Created at: March 1, 2026, 7:36 p.m.