Triple

T35962713
Position Surface form Disambiguated ID Type / Status
Subject Sullivan, Indiana E1040045 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Sullivan Elementary School
Sullivan Elementary School is a primary education school serving young students in the community of Sullivan, Indiana.
E2167200 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: Sullivan Elementary School | Statement: [Sullivan, Indiana, hasEducationalInstitution, Sullivan Elementary School]
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: Sullivan Elementary School
Triple: [Sullivan, Indiana, hasEducationalInstitution, Sullivan Elementary School]
Generated description
Sullivan Elementary School is a primary education school serving young students in the community of Sullivan, Indiana.

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_69f76e26b21081909fd9ffb3aff6c77a completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7abfa8d9c8190853e8e501da0a2d4 completed May 3, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38cb8241308190b1c5ea7f7ff71cef completed June 22, 2026, 5:43 a.m.
NEDg Description generation batch_6a38cdc576708190b838d02980a85c71 completed June 22, 2026, 5:53 a.m.
NED2 Entity disambiguation (via description) batch_6a38ce2bd3fc8190a0e3810da50fd3fb completed June 22, 2026, 5:54 a.m.
Created at: May 3, 2026, 4:07 p.m.