Triple

T26104268
Position Surface form Disambiguated ID Type / Status
Subject Ohio state forests E658493 entity
Predicate contains P35 FINISHED
Object Zaleski State Forest
Zaleski State Forest is one of Ohio’s largest state forests, known for its rugged Appalachian terrain, extensive hiking trails, and rich wildlife habitat.
 E1755821 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: Zaleski State Forest | Statement: [Ohio state forests, contains, Zaleski 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: Zaleski State Forest
Triple: [Ohio state forests, contains, Zaleski State Forest]
Generated description
Zaleski State Forest is one of Ohio’s largest state forests, known for its rugged Appalachian terrain, extensive hiking trails, and rich wildlife habitat.

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_69ee5bc09c288190bc42a11972841383 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f60774d1648190a2616433371e1d51 completed May 2, 2026, 2:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1247d247e88190a8961c9659e45413 completed May 24, 2026, 12:35 a.m.
NEDg Description generation batch_6a124841f5388190bda464ecd74a700e completed May 24, 2026, 12:37 a.m.
NED2 Entity disambiguation (via description) batch_6a1248a2f59c8190af2c3a8c50a36af3 completed May 24, 2026, 12:38 a.m.
Created at: April 26, 2026, 7:57 p.m.