Triple

T26658366
Position Surface form Disambiguated ID Type / Status
Subject Prince George’s County Council District E666571 entity
Predicate appliesLaw P125 FINISHED
Object Prince George’s County Code
Prince George’s County Code is the comprehensive collection of local laws and regulations governing Prince George’s County, Maryland.
E1734649 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: Prince George’s County Code | Statement: [Prince George’s County Council District, appliesLaw, Prince George’s County Code]
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: Prince George’s County Code
Triple: [Prince George’s County Council District, appliesLaw, Prince George’s County Code]
Generated description
Prince George’s County Code is the comprehensive collection of local laws and regulations governing Prince George’s County, Maryland.

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_69ee9cf8c7188190b9b00270a8a89164 completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f616bb8574819087339f6e25346d2c completed May 2, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11ec4ad388819083cb330c6e42bac2 completed May 23, 2026, 6:04 p.m.
NEDg Description generation batch_6a11ecf5d69881908edb6de497f038c9 completed May 23, 2026, 6:07 p.m.
NED2 Entity disambiguation (via description) batch_6a11edfed6288190b0c75e8c4a0216ba completed May 23, 2026, 6:12 p.m.
Created at: April 27, 2026, 2:35 a.m.