Triple

T24741347
Position Surface form Disambiguated ID Type / Status
Subject Maricao, Puerto Rico E618568 entity
Predicate hasProtectedArea P855 FINISHED
Object Monte del Estado Forest
Monte del Estado Forest is a mountainous subtropical forest reserve in western Puerto Rico known for its rich biodiversity, cool climate, and network of hiking trails and scenic viewpoints.
E1647458 NE FINISHED

How this triple was built (2 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: Monte del Estado Forest | Statement: [Maricao, Puerto Rico, hasProtectedArea, Monte del Estado Forest]
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: Monte del Estado Forest
Triple: [Maricao, Puerto Rico, hasProtectedArea, Monte del Estado Forest]
Generated description
Monte del Estado Forest is a mountainous subtropical forest reserve in western Puerto Rico known for its rich biodiversity, cool climate, and network of hiking trails and scenic viewpoints.

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_69e2fab8f95c81908bb9e552cf3280c2 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f41056b2c8819085b4ee2509352786 completed May 1, 2026, 2:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101025cb248190a4ad9b967c188d33 completed May 22, 2026, 8:13 a.m.
NEDg Description generation batch_6a10136f4b048190b4664398b5929656 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a101436b0008190a5e27291df640af5 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 4:14 a.m.