Triple
T15258000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trenitalia |
E364696
|
entity |
| Predicate | subsidiary |
P258
|
FINISHED |
| Object | Netinera |
E510344
|
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: Netinera | Statement: [Trenitalia, subsidiary, Netinera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Netinera Context triple: [Trenitalia, subsidiary, Netinera]
-
A.
Netinera
chosen
Netinera is a major private rail and bus transport company operating regional passenger services across Germany.
-
B.
Netia
Netia is one of Poland’s leading telecommunications providers, offering broadband internet and related services to residential and business customers nationwide.
-
C.
Nete
The Nete is a river in Belgium that flows through the Flemish region and serves as one of the main tributaries forming the Rupel River.
-
D.
Tianeti
Tianeti is a small town and administrative center in eastern Georgia, situated in the mountainous Mtskheta-Mtianeti region.
-
E.
Nanaline
Nanaline was an American socialite and philanthropist associated with the prominent Duke family in the early 20th century.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0084d11148190919eef8e55569bb9 |
completed | April 15, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5f9a0708190bc429692788a63d7 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:13 a.m.