Triple
T129368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fürth |
E2619
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Limoges
Limoges is a historic city in central France renowned for its fine porcelain production and medieval architecture.
|
E49689
|
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: Limoges | Statement: [Fürth, hasTwinTown, Limoges]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Limoges Context triple: [Fürth, hasTwinTown, Limoges]
-
A.
Vichy
Vichy is a spa town in central France renowned for its thermal springs, health resorts, and role as the seat of the World War II Vichy regime.
-
B.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
-
C.
Toulouse
Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
-
D.
Nantes
Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
-
E.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
- 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: Limoges Triple: [Fürth, hasTwinTown, Limoges]
Generated description
Limoges is a historic city in central France renowned for its fine porcelain production and medieval architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Limoges Target entity description: Limoges is a historic city in central France renowned for its fine porcelain production and medieval architecture.
-
A.
Vichy
Vichy is a spa town in central France renowned for its thermal springs, health resorts, and role as the seat of the World War II Vichy regime.
-
B.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
-
C.
Toulouse
Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
-
D.
Nantes
Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
-
E.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
- 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_69a2520c0f3481908b0ed054a2fca8d0 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a2576518e0819096b35d8af7a4d1bd |
completed | Feb. 28, 2026, 2:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a405ef40248190b81d461f3b6d4baa |
completed | March 1, 2026, 9:25 a.m. |
| NEDg | Description generation | batch_69a406e41a18819085bb25ff4b1a0d3c |
completed | March 1, 2026, 9:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a40740cc4c81909e4e3ff05f7e6c20 |
completed | March 1, 2026, 9:30 a.m. |
Created at: Feb. 28, 2026, 2:30 a.m.