Triple
T15187425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marmolada |
E362911
|
entity |
| Predicate | hasLanguageVariantName |
P15
|
FINISHED |
| Object |
Marmolèda
Marmolèda is the Ladin name for Marmolada, the highest mountain in the Dolomites of northeastern Italy.
|
E1142327
|
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: Marmolèda | Statement: [Marmolada, hasLanguageVariantName, Marmolèda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marmolèda Context triple: [Marmolada, hasLanguageVariantName, Marmolèda]
-
A.
Camarasa
Camarasa is a municipality in the province of Lleida, Catalonia, Spain, known for its reservoir and scenic location in the Noguera region.
-
B.
Vallmoll
Vallmoll is a small municipality in the province of Tarragona, within the autonomous community of Catalonia in northeastern Spain.
-
C.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
D.
Tallichet
Tallichet is a surname most notably associated with American actress Margaret Tallichet, who appeared in several films in the late 1930s and early 1940s.
-
E.
La Pintana
La Pintana is a commune in the Santiago Metropolitan Region of Chile, known for its predominantly residential character and significant social and urban development challenges.
- 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: Marmolèda Triple: [Marmolada, hasLanguageVariantName, Marmolèda]
Generated description
Marmolèda is the Ladin name for Marmolada, the highest mountain in the Dolomites of northeastern Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marmolèda Target entity description: Marmolèda is the Ladin name for Marmolada, the highest mountain in the Dolomites of northeastern Italy.
-
A.
Camarasa
Camarasa is a municipality in the province of Lleida, Catalonia, Spain, known for its reservoir and scenic location in the Noguera region.
-
B.
Vallmoll
Vallmoll is a small municipality in the province of Tarragona, within the autonomous community of Catalonia in northeastern Spain.
-
C.
Gironella
Gironella is a small municipality in Catalonia, Spain, known for its historic textile industry and location along the Llobregat River.
-
D.
Tallichet
Tallichet is a surname most notably associated with American actress Margaret Tallichet, who appeared in several films in the late 1930s and early 1940s.
-
E.
La Pintana
La Pintana is a commune in the Santiago Metropolitan Region of Chile, known for its predominantly residential character and significant social and urban development challenges.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0067995fc8190b048f15086bd42f0 |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec895b59c81908a09f8393a35aa13 |
completed | May 9, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_69fec9ece2fc8190811e83185cdb4dfc |
completed | May 9, 2026, 5:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fecc58d7b0819082090b6205b77b16 |
completed | May 9, 2026, 5:55 a.m. |
Created at: April 10, 2026, 3:09 a.m.