Triple

T18068840
Position Surface form Disambiguated ID Type / Status
Subject Orange County, Texas E432365 entity
Predicate hasMajorHighway P385 FINISHED
Object State Highway 73
State Highway 73 is a significant east–west roadway in southeast Texas that connects industrial and coastal communities and serves as a key transportation corridor in the region.
 E2287372 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 73 | Statement: [Orange County, Texas, hasMajorHighway, State Highway 73]
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 73
Triple: [Orange County, Texas, hasMajorHighway, State Highway 73]
Generated description
State Highway 73 is a significant east–west roadway in southeast Texas that connects industrial and coastal communities and serves as a key transportation corridor in the region.

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_69d8b9070cac81909fa9473fb1c3f1c7 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ccebff748190b41d2edd93994c67 completed April 19, 2026, 12:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a47864334008190b4245d660ce3fbc6 completed July 3, 2026, 9:52 a.m.
NEDg Description generation batch_6a478753162c8190bee5906d474c2949 completed July 3, 2026, 9:56 a.m.
NED2 Entity disambiguation (via description) batch_6a4787f3c5ec8190aa5bdb70980ff559 completed July 3, 2026, 9:59 a.m.
Created at: April 10, 2026, 10:26 a.m.