Triple

T35728231
Position Surface form Disambiguated ID Type / Status
Subject Chief Justice of the Connecticut Supreme Court E1032673 entity
Predicate officeHoldersInclude P537 FINISHED
Object Francis M. McDonald Jr.
Francis M. McDonald Jr. is an American jurist who served as a leading figure on Connecticut’s highest court, ultimately becoming its chief justice.
E2296184 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: Francis M. McDonald Jr. | Statement: [Chief Justice of the Connecticut Supreme Court, officeHoldersInclude, Francis M. McDonald Jr.]
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: Francis M. McDonald Jr.
Triple: [Chief Justice of the Connecticut Supreme Court, officeHoldersInclude, Francis M. McDonald Jr.]
Generated description
Francis M. McDonald Jr. is an American jurist who served as a leading figure on Connecticut’s highest court, ultimately becoming its chief justice.

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_69f76e102b5881909e5d63a30a5cecbe completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a132bd04819080edfe6ea2d6ca0b completed May 3, 2026, 7:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a8245527fdc81909e2f6c917766b7a9 completed Aug. 16, 2026, 11:18 p.m.
NEDg Description generation batch_6a8245c891d8819087848b2838db758e completed Aug. 16, 2026, 11:20 p.m.
NED2 Entity disambiguation (via description) batch_6a8245edc6888190b385e28b93c75637 completed Aug. 16, 2026, 11:21 p.m.
Created at: May 3, 2026, 4:05 p.m.