Triple

T17776873
Position Surface form Disambiguated ID Type / Status
Subject Whitehouse, Texas E443792 entity
Predicate roadAccessVia P9041 FINISHED
Object State Highway 110
State Highway 110 is a Texas state highway that runs through East Texas, connecting communities such as Whitehouse and serving as a regional transportation route.
 E2287053 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 Highway 110 | Statement: [Whitehouse, Texas, roadAccessVia, State Highway 110]
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 Highway 110
Triple: [Whitehouse, Texas, roadAccessVia, State Highway 110]
Generated description
State Highway 110 is a Texas state highway that runs through East Texas, connecting communities such as Whitehouse and serving as a regional transportation 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_69d8b9ef17708190bdf7e2adbf14ddc2 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4871d43a481908aacde69bd8091b0 completed April 19, 2026, 7:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a475ba1fa548190afbd4e04169f2ef3 completed July 3, 2026, 6:50 a.m.
NEDg Description generation batch_6a475c6b9bac819096e18bf372d7cd0d completed July 3, 2026, 6:53 a.m.
NED2 Entity disambiguation (via description) batch_6a475cb92ecc8190a5e1a5874de4efd0 completed July 3, 2026, 6:54 a.m.
Created at: April 10, 2026, 10:12 a.m.