Triple

T34720721
Position Surface form Disambiguated ID Type / Status
Subject Iron River, Michigan E1000904 entity
Predicate namedAfter P63 FINISHED
Object Iron River
Iron River is a small city in Michigan’s Upper Peninsula known for its historic iron mining roots and surrounding forests and lakes.
 E2184741 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: Iron River | Statement: [Iron River, Michigan, namedAfter, Iron River]
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: Iron River
Triple: [Iron River, Michigan, namedAfter, Iron River]
Generated description
Iron River is a small city in Michigan’s Upper Peninsula known for its historic iron mining roots and surrounding forests and lakes.

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_69f76daeb6e48190a4c9a6b0edc80f72 completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f77993365c8190aa957a1473ff1605 completed May 3, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39cfac0f0081909ea94851419ad716 completed June 23, 2026, 12:13 a.m.
NEDg Description generation batch_6a39d0835f7881909e2f9f19aa336e79 completed June 23, 2026, 12:17 a.m.
NED2 Entity disambiguation (via description) batch_6a39d149d0c88190b232b80550967869 completed June 23, 2026, 12:20 a.m.
Created at: May 3, 2026, 3:59 p.m.