Triple

T25287852
Position Surface form Disambiguated ID Type / Status
Subject APSA Harold D. Lasswell Award E633992 entity
Predicate relatedOrganization P37 FINISHED
Object APSA Public Policy section
The APSA Public Policy section is a division of the American Political Science Association that brings together scholars focused on the study, teaching, and advancement of public policy research.
E1638949 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: APSA Public Policy section | Statement: [APSA Harold D. Lasswell Award, relatedOrganization, APSA Public Policy section]
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: APSA Public Policy section
Triple: [APSA Harold D. Lasswell Award, relatedOrganization, APSA Public Policy section]
Generated description
The APSA Public Policy section is a division of the American Political Science Association that brings together scholars focused on the study, teaching, and advancement of public policy research.

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_69e75a9402fc81909362ca85277c06d9 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f48e0b00ec8190ae6c45f2ce24b727 completed May 1, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1068050a68819085479811fc97246d completed May 22, 2026, 2:28 p.m.
NEDg Description generation batch_6a1068afc81c819099129f1e8886461d completed May 22, 2026, 2:31 p.m.
NED2 Entity disambiguation (via description) batch_6a106924c84c819093d89ae18c9aa37b completed May 22, 2026, 2:33 p.m.
Created at: April 21, 2026, 1:19 p.m.