Triple
T661391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TGV |
E11761
|
entity |
| Predicate | notableRoute |
P22
|
FINISHED |
| Object |
Paris–Lille
Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
|
E87143
|
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: Paris–Lille | Statement: [TGV, notableRoute, Paris–Lille]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paris–Lille Context triple: [TGV, notableRoute, Paris–Lille]
-
A.
New York–Paris
New York–Paris is a major transatlantic air route connecting the United States and France, linking New York City with the French capital.
-
B.
Lille
Lille is a historic industrial and cultural hub in northern France, known for its Flemish-influenced architecture, large student population, and role as a major European transport crossroads.
-
C.
Le Mans
Le Mans is a historic city in northwestern France best known for its annual 24 Hours of Le Mans endurance sports car race.
-
D.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
E.
Paris–Roubaix
Paris–Roubaix is one of cycling’s oldest and most prestigious one-day “Monument” races, famed for its brutal cobblestone sectors and often unpredictable, harsh conditions.
- 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: Paris–Lille Triple: [TGV, notableRoute, Paris–Lille]
Generated description
Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paris–Lille Target entity description: Paris–Lille is a major high-speed rail corridor in northern France connecting the capital Paris with the city of Lille.
-
A.
New York–Paris
New York–Paris is a major transatlantic air route connecting the United States and France, linking New York City with the French capital.
-
B.
Lille
Lille is a historic industrial and cultural hub in northern France, known for its Flemish-influenced architecture, large student population, and role as a major European transport crossroads.
-
C.
Le Mans
Le Mans is a historic city in northwestern France best known for its annual 24 Hours of Le Mans endurance sports car race.
-
D.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
-
E.
Paris–Roubaix
Paris–Roubaix is one of cycling’s oldest and most prestigious one-day “Monument” races, famed for its brutal cobblestone sectors and often unpredictable, harsh conditions.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49fa954988190841740a587ace466 |
completed | March 1, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a63747e47481909877b49507b67c2c |
completed | March 3, 2026, 1:20 a.m. |
| NEDg | Description generation | batch_69a645cff2a481908aa0b0cfde78c929 |
completed | March 3, 2026, 2:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a64656026c8190834af887720f3a0a |
completed | March 3, 2026, 2:24 a.m. |
Created at: March 1, 2026, 7:36 p.m.