Triple
T6637046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milan Bergamo Airport |
E150483
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Milan |
E11464
|
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: Milan | Statement: [Milan Bergamo Airport, servesCity, Milan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Milan Context triple: [Milan Bergamo Airport, servesCity, Milan]
-
A.
Milan
Milan is a masculine given name of Slavic origin, commonly used in Central and Eastern Europe.
-
B.
Milan
chosen
Milan is a major Italian metropolis renowned as a global center for fashion, design, finance, and culture.
-
C.
Milan
Milan is a village in northern Ohio best known as the birthplace of inventor Thomas Edison and for its historic canal-era architecture.
-
D.
Milano
Milano is a popular line of chocolate-filled sandwich cookies produced by Pepperidge Farm, a subsidiary of Campbell Soup Company.
-
E.
Turin
Turin is a major city in northern Italy known for its rich history, Baroque architecture, automotive industry, and role as a cultural and economic hub.
- 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_69c687f0ceb08190bf40807bfc605fa5 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afcf439c8190b9334b34774da821 |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e41434108190b6c329544764dd2c |
completed | March 27, 2026, 8:09 p.m. |
Created at: March 27, 2026, 1:59 p.m.