Triple

T14696065
Position Surface form Disambiguated ID Type / Status
Subject Don DeLillo bibliography E345161 entity
Predicate includesWork P2011 FINISHED
Object Valparaiso E267211 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: Valparaiso | Statement: [Don DeLillo bibliography, includesWork, Valparaiso]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valparaiso
Context triple: [Don DeLillo bibliography, includesWork, Valparaiso]
  • A. Valparaiso, Indiana chosen
    Valparaiso, Indiana is a small city in northwestern Indiana known for hosting Valparaiso University and serving as a regional cultural and educational center.
  • B. Mauckport, Indiana
    Mauckport, Indiana is a small historic town on the Ohio River in Harrison County, known for its riverfront location opposite Brandenburg, Kentucky.
  • C. Michigan City, Indiana
    Michigan City, Indiana is a small city on the southern shore of Lake Michigan known as a gateway to the Indiana Dunes and a regional hub for lakefront recreation and tourism.
  • D. Evansville
    Evansville is a major city in southwestern Indiana located along the Ohio River and serving as a regional economic and cultural hub.
  • E. La Porte, Indiana
    La Porte, Indiana is a small Midwestern city in northwestern Indiana known for its historic downtown, surrounding lakes, and role as a regional industrial and commercial center.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb58855e081908b38f9515db5677f completed April 14, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdfb807af081908dd56caf3d06550f completed May 8, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:28 a.m.