Triple

T26432042
Position Surface form Disambiguated ID Type / Status
Subject Thelma Todd E664535 entity
Predicate participatedIn P149 FINISHED
Object Miss Massachusetts beauty pageant
The Miss Massachusetts beauty pageant is a state-level competition that selects Massachusetts’s representative for the Miss America national scholarship pageant.
E1726512 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: Miss Massachusetts beauty pageant | Statement: [Thelma Todd, participatedIn, Miss Massachusetts beauty pageant]
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: Miss Massachusetts beauty pageant
Triple: [Thelma Todd, participatedIn, Miss Massachusetts beauty pageant]
Generated description
The Miss Massachusetts beauty pageant is a state-level competition that selects Massachusetts’s representative for the Miss America national scholarship pageant.

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_69ee883ad6a4819088f918e76122d690 completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f611bebb8481909d7be89946db38d1 completed May 2, 2026, 3:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11aeca50ac81908f37bebed39d81e8 completed May 23, 2026, 1:42 p.m.
NEDg Description generation batch_6a11b290f4388190b733d4ce4f4b6b25 completed May 23, 2026, 1:58 p.m.
NED2 Entity disambiguation (via description) batch_6a11b32b04448190803e9a66a4931bbd completed May 23, 2026, 2:01 p.m.
Created at: April 26, 2026, 11:50 p.m.