Triple

T27443313
Position Surface form Disambiguated ID Type / Status
Subject Interstate 410 E690998 entity
Predicate alsoKnownAs P39 FINISHED
Object Loop 410
Loop 410 is a beltway freeway encircling much of San Antonio, Texas, serving as a major route for local and through traffic.
 E1773759 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: Loop 410 | Statement: [Interstate 410, alsoKnownAs, Loop 410]
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: Loop 410
Triple: [Interstate 410, alsoKnownAs, Loop 410]
Generated description
Loop 410 is a beltway freeway encircling much of San Antonio, Texas, serving as a major route for local and through traffic.

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_69ef5200fa0481908e28508d6e2c149e completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d9002a08190ba5f034dff23968c completed May 2, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12b259e0648190a43102b1d9948c59 completed May 24, 2026, 8:10 a.m.
NEDg Description generation batch_6a12b3f7b0748190bfa5a2c631d0836d completed May 24, 2026, 8:16 a.m.
NED2 Entity disambiguation (via description) batch_6a12b50444b88190947f0c2989954233 completed May 24, 2026, 8:21 a.m.
Created at: April 27, 2026, 12:45 p.m.