Triple

T37891066
Position Surface form Disambiguated ID Type / Status
Subject Texas State Highway 22 E945137 entity
Predicate abbreviation P43 FINISHED
Object SH 22
SH 22 is a Texas state highway that runs east–west across central Texas, connecting rural communities and regional routes.
E2246086 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: SH 22 | Statement: [Texas State Highway 22, abbreviation, SH 22]
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: SH 22
Triple: [Texas State Highway 22, abbreviation, SH 22]
Generated description
SH 22 is a Texas state highway that runs east–west across central Texas, connecting rural communities 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_69f76ef0e8708190987c7254ed8c7abe completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbd2672d48190a30716eb4dda853f completed May 6, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41043592148190b93984ebc6db94cd completed June 28, 2026, 11:23 a.m.
NEDg Description generation batch_6a4104af9ac88190a7ffe368c46f0f8d completed June 28, 2026, 11:25 a.m.
NED2 Entity disambiguation (via description) batch_6a41052129a08190a8e4d598dcd259b2 completed June 28, 2026, 11:27 a.m.
Created at: May 3, 2026, 4:19 p.m.