Triple

T25626817
Position Surface form Disambiguated ID Type / Status
Subject Ilion, New York E642454 entity
Predicate hasPoliceDepartment P13246 FINISHED
Object Ilion Police Department
The Ilion Police Department is the local law enforcement agency responsible for public safety and policing services in the village of Ilion, New York.
E1688207 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: Ilion Police Department | Statement: [Ilion, New York, hasPoliceDepartment, Ilion Police Department]
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: Ilion Police Department
Triple: [Ilion, New York, hasPoliceDepartment, Ilion Police Department]
Generated description
The Ilion Police Department is the local law enforcement agency responsible for public safety and policing services in the village of Ilion, New York.

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_69e77e7bd4548190a0c691b8a2f27ff1 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fa248b6c8190b79173bc9d806f21 completed May 2, 2026, 1:20 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10b78370b88190ac90e578260a6606 completed May 22, 2026, 8:07 p.m.
NEDg Description generation batch_6a10b9a7f4088190baf28e4e0f47c277 completed May 22, 2026, 8:16 p.m.
NED2 Entity disambiguation (via description) batch_6a10ba344df081908266aaa1920d9f3d completed May 22, 2026, 8:19 p.m.
Created at: April 21, 2026, 5:15 p.m.