Triple

T24445835
Position Surface form Disambiguated ID Type / Status
Subject Walters Middle School E616401 entity
Predicate serviceArea P82 FINISHED
Object Fremont, California area
The Fremont, California area is a diverse suburban region in the southeastern San Francisco Bay Area known for its strong schools, tech-industry presence, and family-oriented communities.
 E1641166 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: Fremont, California area | Statement: [Walters Middle School, serviceArea, Fremont, California area]
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: Fremont, California area
Triple: [Walters Middle School, serviceArea, Fremont, California area]
Generated description
The Fremont, California area is a diverse suburban region in the southeastern San Francisco Bay Area known for its strong schools, tech-industry presence, and family-oriented 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_69e2d7edca608190aafefc8877a1b4da completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29853a3ac81908bb5398539a44cd6 completed April 29, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0ff847fa7c8190bf7f122fd375c008 completed May 22, 2026, 6:31 a.m.
NEDg Description generation batch_6a0ff956f6e48190950c5bace85c9669 completed May 22, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff9feda34819084e79982606c3972 completed May 22, 2026, 6:38 a.m.
Created at: April 18, 2026, 2:17 a.m.