Triple

T34344005
Position Surface form Disambiguated ID Type / Status
Subject Coffee County, Tennessee E881377 entity
Predicate majorHighway P385 FINISHED
Object State Route 53
State Route 53 is a primary north–south state highway in Tennessee that connects several rural communities and provides regional access across multiple counties.
 E2296967 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 53 | Statement: [Coffee County, Tennessee, majorHighway, State Route 53]
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 53
Triple: [Coffee County, Tennessee, majorHighway, State Route 53]
Generated description
State Route 53 is a primary north–south state highway in Tennessee that connects several rural communities and provides regional access across multiple counties.

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_69f349bc55e881908c8e338ef76b0043 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f713edfc548190bfa0092d54369302 completed May 3, 2026, 9:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a82e9e7e43c819089b818545627df67 completed Aug. 17, 2026, 11 a.m.
NEDg Description generation batch_6a82ea273b348190bacb3c9ecbccf1f6 completed Aug. 17, 2026, 11:01 a.m.
NED2 Entity disambiguation (via description) batch_6a82eb240f2c8190a8e59e946dfaa143 completed Aug. 17, 2026, 11:06 a.m.
Created at: May 1, 2026, 1:58 a.m.