Triple
T19250475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melegnano |
E481376
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Cerro al Lambro
Cerro al Lambro is a small municipality in the Metropolitan City of Milan in the Lombardy region of northern Italy.
|
E1368377
|
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: Cerro al Lambro | Statement: [Melegnano, near, Cerro al Lambro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cerro al Lambro Context triple: [Melegnano, near, Cerro al Lambro]
-
A.
Cerro López
Cerro López is a prominent mountain peak in Argentina’s Patagonia region, known for its panoramic views over Nahuel Huapi Lake and popular hiking and skiing routes.
-
B.
Cerro
Cerro is a Spanish term commonly used in place names throughout Latin America to denote a hill or mountain.
-
C.
Cerro San Rafael
Cerro San Rafael is a prominent mountain peak in northeastern Mexico, notable for being the highest summit in the Sierra Madre Oriental range.
-
D.
Cerro San Valentín
Cerro San Valentín is the highest mountain in Chilean Patagonia, rising prominently within the Northern Patagonian Ice Field of southern Chile.
-
E.
Cerro La Campana
Cerro La Campana is a prominent Chilean mountain famed for its distinctive bell-shaped peak, rich biodiversity, and panoramic views that inspired Charles Darwin.
- 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: Cerro al Lambro Triple: [Melegnano, near, Cerro al Lambro]
Generated description
Cerro al Lambro is a small municipality in the Metropolitan City of Milan in the Lombardy region of northern Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cerro al Lambro Target entity description: Cerro al Lambro is a small municipality in the Metropolitan City of Milan in the Lombardy region of northern Italy.
-
A.
Cerro López
Cerro López is a prominent mountain peak in Argentina’s Patagonia region, known for its panoramic views over Nahuel Huapi Lake and popular hiking and skiing routes.
-
B.
Cerro
Cerro is a Spanish term commonly used in place names throughout Latin America to denote a hill or mountain.
-
C.
Cerro San Rafael
Cerro San Rafael is a prominent mountain peak in northeastern Mexico, notable for being the highest summit in the Sierra Madre Oriental range.
-
D.
Cerro San Valentín
Cerro San Valentín is the highest mountain in Chilean Patagonia, rising prominently within the Northern Patagonian Ice Field of southern Chile.
-
E.
Cerro La Campana
Cerro La Campana is a prominent Chilean mountain famed for its distinctive bell-shaped peak, rich biodiversity, and panoramic views that inspired Charles Darwin.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb3001308190913e24343769be8d |
completed | April 20, 2026, 10:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a070e6900048190942b665bcaf81af8 |
completed | May 15, 2026, 12:15 p.m. |
| NEDg | Description generation | batch_6a070fc891448190a5061c0d340548d5 |
completed | May 15, 2026, 12:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0710836b608190a89900ba3f6916bb |
completed | May 15, 2026, 12:24 p.m. |
Created at: April 10, 2026, 1:27 p.m.