Triple

T24862264
Position Surface form Disambiguated ID Type / Status
Subject Maui road network E622183 entity
Predicate hasRouteNumber P1864 FINISHED
Object Hawaii Route 30
Hawaii Route 30 is a state highway on the island of Maui that runs along the island’s western coast, connecting key resort areas and communities.
 E1699319 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: Hawaii Route 30 | Statement: [Maui road network, hasRouteNumber, Hawaii Route 30]
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: Hawaii Route 30
Triple: [Maui road network, hasRouteNumber, Hawaii Route 30]
Generated description
Hawaii Route 30 is a state highway on the island of Maui that runs along the island’s western coast, connecting key resort areas and communities.

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_69e2fac350d08190b3affde1b451a8c5 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f422ed8d9c8190ba8c8de664a66031 completed May 1, 2026, 3:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10ec7d5db48190bb18a72b59342739 completed May 22, 2026, 11:53 p.m.
NEDg Description generation batch_6a10ed4abcc88190a6da8d038829a22d completed May 22, 2026, 11:56 p.m.
NED2 Entity disambiguation (via description) batch_6a10edb1ca0c81909f567f40e6601be6 completed May 22, 2026, 11:58 p.m.
Created at: April 18, 2026, 5:22 a.m.