Triple

T37143435
Position Surface form Disambiguated ID Type / Status
Subject Abbott v. Burke line of cases E920176 entity
Predicate alsoIncludes P1393 FINISHED
Object Abbott V
Abbott V is a landmark New Jersey Supreme Court decision in the Abbott v. Burke school-funding litigation that expanded remedies for students in poor urban districts, including requirements for supplemental programs and facilities improvements.
 E2214840 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: Abbott V | Statement: [Abbott v. Burke line of cases, alsoIncludes, Abbott V]
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: Abbott V
Triple: [Abbott v. Burke line of cases, alsoIncludes, Abbott V]
Generated description
Abbott V is a landmark New Jersey Supreme Court decision in the Abbott v. Burke school-funding litigation that expanded remedies for students in poor urban districts, including requirements for supplemental programs and facilities improvements.

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_69f76e9e9d008190a250b0387c992c74 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb3066aaa4819094d8a3462b024756 completed May 6, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3f6a2d35848190b6410e4ac53df115 completed June 27, 2026, 6:14 a.m.
NEDg Description generation batch_6a3f6c091a548190809a8a3b4f142e83 completed June 27, 2026, 6:22 a.m.
NED2 Entity disambiguation (via description) batch_6a3ffa5151a8819081fd0b81ff6d3749 completed June 27, 2026, 4:29 p.m.
Created at: May 3, 2026, 4:15 p.m.