Triple

T19899700
Position Surface form Disambiguated ID Type / Status
Subject Ware County E478248 entity
Predicate hasMajorHighway P385 FINISHED
Object State Route 122
State Route 122 is a Georgia state highway that runs across several south Georgia counties, serving as an important east–west connector route.
 E2282811 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 122 | Statement: [Ware County, hasMajorHighway, State Route 122]
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 122
Triple: [Ware County, hasMajorHighway, State Route 122]
Generated description
State Route 122 is a Georgia state highway that runs across several south Georgia counties, serving as an important east–west connector route.

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_69d8e520682081909892916424699bd5 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65940cf8c8190b74e51635410e48a completed April 20, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a422b9edbd88190b303e6d47a98aa99 completed June 29, 2026, 8:23 a.m.
NEDg Description generation batch_6a422c27fcd48190b18dc28056a71a96 completed June 29, 2026, 8:26 a.m.
NED2 Entity disambiguation (via description) batch_6a422c7bb5008190ad708c4f89958071 completed June 29, 2026, 8:27 a.m.
Created at: April 10, 2026, 1:52 p.m.