Triple

T22828018
Position Surface form Disambiguated ID Type / Status
Subject Ohio State Route 29 E565713 entity
Predicate abbreviation P43 FINISHED
Object State Route 29
State Route 29 is a state highway in Ohio that runs east–west across the western part of the state, connecting several small towns and regional routes.
 E2289454 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: State Route 29 | Statement: [Ohio State Route 29, abbreviation, State Route 29]
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: State Route 29
Triple: [Ohio State Route 29, abbreviation, State Route 29]
Generated description
State Route 29 is a state highway in Ohio that runs east–west across the western part of the state, connecting several small towns and regional routes.

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_69e24585ab1c81909b2b5065d15805d5 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17e2914188190be6cdbd8167cd806 completed April 29, 2026, 3:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5b3d9a16cc8190b4b19eaebb3a2d77 completed July 18, 2026, 8:47 a.m.
NEDg Description generation batch_6a5b3ea2454081909cc8d85ce4498662 completed July 18, 2026, 8:51 a.m.
NED2 Entity disambiguation (via description) batch_6a5b40b69724819097275cec6e80413b completed July 18, 2026, 9 a.m.
Created at: April 17, 2026, 3:34 p.m.