Triple

T27779038
Position Surface form Disambiguated ID Type / Status
Subject Government of the City of St. Louis E699281 entity
Predicate hasDepartment P35 FINISHED
Object St. Louis Treasurer’s Office
The St. Louis Treasurer’s Office is a municipal financial agency responsible for managing the city’s funds, investments, and often parking and revenue-related operations.
E1791372 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: St. Louis Treasurer’s Office | Statement: [Government of the City of St. Louis, hasDepartment, St. Louis Treasurer’s Office]
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: St. Louis Treasurer’s Office
Triple: [Government of the City of St. Louis, hasDepartment, St. Louis Treasurer’s Office]
Generated description
The St. Louis Treasurer’s Office is a municipal financial agency responsible for managing the city’s funds, investments, and often parking and revenue-related operations.

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_69ef6a4b5a9081909c9111396c2be3d2 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f637cc7028819092c523169024e360 completed May 2, 2026, 5:43 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12f71897c08190b172e367286fc595 completed May 24, 2026, 1:03 p.m.
NEDg Description generation batch_6a12f79fed1c81908af492a3fd35f82d completed May 24, 2026, 1:05 p.m.
NED2 Entity disambiguation (via description) batch_6a12fbae881c8190a13234bf6ad26f8f completed May 24, 2026, 1:22 p.m.
Created at: April 27, 2026, 5:08 p.m.