Triple

T24229438
Position Surface form Disambiguated ID Type / Status
Subject 1986 Nebraska gubernatorial election E601685 entity
Predicate loser P356 FINISHED
Object Helen Boosalis
Helen Boosalis was an American politician and former mayor of Lincoln, Nebraska, who became the first woman nominated by a major party for governor of Nebraska.
E1648335 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: Helen Boosalis | Statement: [1986 Nebraska gubernatorial election, loser, Helen Boosalis]
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: Helen Boosalis
Triple: [1986 Nebraska gubernatorial election, loser, Helen Boosalis]
Generated description
Helen Boosalis was an American politician and former mayor of Lincoln, Nebraska, who became the first woman nominated by a major party for governor of Nebraska.

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_69e29538aafc8190a2386fdebbd1393b completed April 17, 2026, 8:16 p.m.
NER Named-entity recognition batch_69f287e214e88190bf6d5b0f5f489f73 completed April 29, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a100fd3e4008190991997c66a712ae5 completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136871588190b4e4b4618ab7a400 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10140b2fec8190aa6d805f54926b56 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 12:01 a.m.