Triple
T1841497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TER |
E41185
|
entity |
| Predicate | hasSubclass |
P1244
|
FINISHED |
| Object |
TER Bretagne
TER Bretagne is the regional rail network operated by SNCF that provides passenger train services throughout the Brittany region of France.
|
E206986
|
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: TER Bretagne | Statement: [TER, hasSubclass, TER Bretagne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TER Bretagne Context triple: [TER, hasSubclass, TER Bretagne]
-
A.
Le Breton
Le Breton is a French surname borne by various notable figures, including publishers, politicians, and artists.
-
B.
Mor Breizh
Mor Breizh is the Breton name for the English Channel, the arm of the Atlantic Ocean that separates southern England from northern France.
-
C.
Brionnais
Brionnais is a historic rural region in eastern France known for its Romanesque churches, traditional stone villages, and Charolais cattle farming.
-
D.
Brittany
Brittany is a historic cultural region in northwest France known for its distinct Celtic heritage, Breton language, rugged coastline, and strong Catholic traditions.
-
E.
Breton
Breton is a Celtic language spoken primarily in Brittany, France, known for its close relation to Cornish and Welsh.
- 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: TER Bretagne Triple: [TER, hasSubclass, TER Bretagne]
Generated description
TER Bretagne is the regional rail network operated by SNCF that provides passenger train services throughout the Brittany region of France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: TER Bretagne Target entity description: TER Bretagne is the regional rail network operated by SNCF that provides passenger train services throughout the Brittany region of France.
-
A.
Le Breton
Le Breton is a French surname borne by various notable figures, including publishers, politicians, and artists.
-
B.
Mor Breizh
Mor Breizh is the Breton name for the English Channel, the arm of the Atlantic Ocean that separates southern England from northern France.
-
C.
Brionnais
Brionnais is a historic rural region in eastern France known for its Romanesque churches, traditional stone villages, and Charolais cattle farming.
-
D.
Brittany
Brittany is a historic cultural region in northwest France known for its distinct Celtic heritage, Breton language, rugged coastline, and strong Catholic traditions.
-
E.
Breton
Breton is a Celtic language spoken primarily in Brittany, France, known for its close relation to Cornish and Welsh.
- 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_69a88647f9388190909bc36e795bdaec |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb03e7a7481909c5b902034390ef1 |
completed | March 7, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adc9bb92a88190a00b102d3be0383c |
completed | March 8, 2026, 7:10 p.m. |
| NEDg | Description generation | batch_69adcaf078a0819082c4bb48a3820ada |
completed | March 8, 2026, 7:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adce8a68848190ab56f5df7311dbca |
completed | March 8, 2026, 7:31 p.m. |
Created at: March 4, 2026, 7:33 p.m.