Triple
T18328945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lingotto district |
E439082
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Turin city center |
—
|
NE NERFINISHED |
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: Turin city center | Statement: [Lingotto district, near, Turin city center]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Turin city center Context triple: [Lingotto district, near, Turin city center]
-
A.
Centro storico di Torino
chosen
Centro storico di Torino is the historic center of Turin, Italy, known for its elegant squares, Baroque architecture, and dense concentration of cultural, commercial, and civic landmarks.
-
B.
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.
-
C.
Turin
Turin is a small town located in Coweta County in the U.S. state of Georgia.
-
D.
Turin
Turin is the codename for a generation of AMD EPYC server processors based on the Zen 5 architecture, targeting high-performance and data center workloads.
-
E.
Milan city centre
Milan city centre is the historic and commercial heart of Milan, known for its dense network of streets, major business districts, and proximity to landmarks like the Duomo and the fashion quadrilateral.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50aaceb4c81909c4a6b2790602d2e |
completed | April 19, 2026, 5:02 p.m. |
Created at: April 10, 2026, 10:36 a.m.