Triple

T24375970
Position Surface form Disambiguated ID Type / Status
Subject Pennsylvania state forests E614475 entity
Predicate hasPart P35 FINISHED
Object Bald Eagle State Forest
Bald Eagle State Forest is a large public forest in central Pennsylvania known for its rugged ridges, extensive trail systems, and diverse outdoor recreation opportunities.
E1631270 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: Bald Eagle State Forest | Statement: [Pennsylvania state forests, hasPart, Bald Eagle State 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: Bald Eagle State Forest
Triple: [Pennsylvania state forests, hasPart, Bald Eagle State Forest]
Generated description
Bald Eagle State Forest is a large public forest in central Pennsylvania known for its rugged ridges, extensive trail systems, and diverse outdoor recreation opportunities.

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_69e2d7e1e010819098b95eb3f905943d completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f293d73c0c8190b80e257845a04c57 completed April 29, 2026, 11:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fd678ee848190a3c4cae2749997c0 completed May 22, 2026, 4:07 a.m.
NEDg Description generation batch_6a0fd7f84a908190a128494e4b442ada completed May 22, 2026, 4:13 a.m.
NED2 Entity disambiguation (via description) batch_6a0fd8d0f6848190a77aff96b4fbcc3d completed May 22, 2026, 4:17 a.m.
Created at: April 18, 2026, 2:02 a.m.