Triple

T26775756
Position Surface form Disambiguated ID Type / Status
Subject National Conference of Bar Examiners E670114 entity
Predicate hasTestComponent P113722 FINISHED
Object MPT
The MPT (Multistate Performance Test) is a practical law exam component that assesses bar candidates’ ability to apply legal skills to realistic, task-based scenarios using provided case files and statutes.
E1741239 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: MPT | Statement: [National Conference of Bar Examiners, hasTestComponent, MPT]
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: MPT
Triple: [National Conference of Bar Examiners, hasTestComponent, MPT]
Generated description
The MPT (Multistate Performance Test) is a practical law exam component that assesses bar candidates’ ability to apply legal skills to realistic, task-based scenarios using provided case files and statutes.

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_69eeb31c925881909b597f6e40056d28 completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f6b3ad0d0481909f2cf7d931a3a418 completed May 3, 2026, 2:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1209581948819096c70dcfc1d51245 completed May 23, 2026, 8:08 p.m.
NEDg Description generation batch_6a120a567424819083cc2364aa6ec9da completed May 23, 2026, 8:13 p.m.
NED2 Entity disambiguation (via description) batch_6a120b5561cc81909195b6d74ec74b50 completed May 23, 2026, 8:17 p.m.
Created at: April 27, 2026, 4:04 a.m.