Triple

T910004
Position Surface form Disambiguated ID Type / Status
Subject Yolo County E19636 entity
Predicate countySeat P383 FINISHED
Object Woodland
Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
 E107447 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: Woodland | Statement: [Yolo County, countySeat, Woodland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Woodland
Context triple: [Yolo County, countySeat, Woodland]
  • A. North Woods
    North Woods is a large, wooded section of New York City's Central Park designed to evoke a natural forest retreat within the urban landscape.
  • B. South Woods
    South Woods is a wooded area within New York City's Central Park known for its naturalistic landscape and tranquil, forest-like setting.
  • C. Rumsey Woods
    Rumsey Woods is a wooded area within Buffalo’s historic park and parkway system, known for its natural landscape and recreational green space.
  • D. Argonne Forest
    Argonne Forest is a wooded region in northeastern France that was a major World War I battlefield, particularly known for the Meuse-Argonne Offensive.
  • E. Barnsdale Forest
    Barnsdale Forest is a historic woodland area in South Yorkshire, England, traditionally associated with the legendary outlaw Robin Hood.
  • 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: Woodland
Triple: [Yolo County, countySeat, Woodland]
Generated description
Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Woodland
Target entity description: Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
  • A. North Woods
    North Woods is a large, wooded section of New York City's Central Park designed to evoke a natural forest retreat within the urban landscape.
  • B. South Woods
    South Woods is a wooded area within New York City's Central Park known for its naturalistic landscape and tranquil, forest-like setting.
  • C. Rumsey Woods
    Rumsey Woods is a wooded area within Buffalo’s historic park and parkway system, known for its natural landscape and recreational green space.
  • D. Argonne Forest
    Argonne Forest is a wooded region in northeastern France that was a major World War I battlefield, particularly known for the Meuse-Argonne Offensive.
  • E. Barnsdale Forest
    Barnsdale Forest is a historic woodland area in South Yorkshire, England, traditionally associated with the legendary outlaw Robin Hood.
  • 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_69a4939f91a08190ba68c2c81eab90fe completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b2dca5208190bc9f17cd9dd6a98f completed March 1, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7c73d5bdc8190828cdf9f54e33a46 completed March 4, 2026, 5:46 a.m.
NEDg Description generation batch_69a7c8c148708190bfdee5a55ab6d149 completed March 4, 2026, 5:53 a.m.
NED2 Entity disambiguation (via description) batch_69a7ca1e37f4819086cda13a446642e2 completed March 4, 2026, 5:58 a.m.
Created at: March 1, 2026, 7:39 p.m.