Triple
T15250584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trenord |
E364507
|
entity |
| Predicate | formedByMergerOf |
P77
|
FINISHED |
| Object |
LeNord
LeNord was a regional Italian railway company that operated commuter and regional train services in Lombardy before being merged into Trenord.
|
E1146222
|
NE FINISHED |
How this triple was built (4 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: LeNord | Statement: [Trenord, formedByMergerOf, LeNord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LeNord Context triple: [Trenord, formedByMergerOf, LeNord]
-
A.
Trou-du-Nord
Trou-du-Nord is a commune in northeastern Haiti known for its agricultural activity and proximity to historic sites from the colonial era.
-
B.
Auregnais
Auregnais is an extinct Norman dialect once spoken on the Channel Island of Alderney.
-
C.
Acul-du-Nord
Acul-du-Nord is a commune in northern Haiti known for its agricultural activities and proximity to the historic city of Cap-Haïtien.
-
D.
Nord
Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
-
E.
Nord
Nord is an Italian publishing imprint known for releasing a wide range of fiction and non-fiction titles under the Gruppo Editoriale Mauri Spagnol umbrella.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: LeNord Triple: [Trenord, formedByMergerOf, LeNord]
Generated description
LeNord was a regional Italian railway company that operated commuter and regional train services in Lombardy before being merged into Trenord.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LeNord Target entity description: LeNord was a regional Italian railway company that operated commuter and regional train services in Lombardy before being merged into Trenord.
-
A.
Trou-du-Nord
Trou-du-Nord is a commune in northeastern Haiti known for its agricultural activity and proximity to historic sites from the colonial era.
-
B.
Auregnais
Auregnais is an extinct Norman dialect once spoken on the Channel Island of Alderney.
-
C.
Acul-du-Nord
Acul-du-Nord is a commune in northern Haiti known for its agricultural activities and proximity to the historic city of Cap-Haïtien.
-
D.
Nord
Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
-
E.
Nord
Nord is an Italian publishing imprint known for releasing a wide range of fiction and non-fiction titles under the Gruppo Editoriale Mauri Spagnol umbrella.
- F. None of above. chosen
Provenance (5 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f62b9c8190b9ad40e2d1912b63 |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5f184d481909eb4294ee3648226 |
completed | May 9, 2026, 7:44 a.m. |
| NEDg | Description generation | batch_69fee7eabf908190b9248f397319eb6b |
completed | May 9, 2026, 7:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fee83bbff481908e297e2c4b2811fb |
completed | May 9, 2026, 7:54 a.m. |
Created at: April 10, 2026, 3:13 a.m.