Triple

T26146077
Position Surface form Disambiguated ID Type / Status
Subject Tristram Dalton E659665 entity
Predicate mother P120 FINISHED
Object Mary Little Dalton
Mary Little Dalton was the mother of American politician and U.S. Senator Tristram Dalton of Massachusetts.
 E1755352 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: Mary Little Dalton | Statement: [Tristram Dalton, mother, Mary Little Dalton]
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: Mary Little Dalton
Triple: [Tristram Dalton, mother, Mary Little Dalton]
Generated description
Mary Little Dalton was the mother of American politician and U.S. Senator Tristram Dalton of Massachusetts.

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_69ee5bc496a88190af7deb7ab5e081de completed April 26, 2026, 6:39 p.m.
NER Named-entity recognition batch_69f60be8bd208190b08271f84710afca completed May 2, 2026, 2:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123a89625c81908e9daeced9437d5e completed May 23, 2026, 11:38 p.m.
NEDg Description generation batch_6a123c27062881908664273fcb5ea8b8 completed May 23, 2026, 11:45 p.m.
NED2 Entity disambiguation (via description) batch_6a123cea536c81908bfb43ef2224a964 completed May 23, 2026, 11:48 p.m.
Created at: April 26, 2026, 8:22 p.m.