Triple

T24625659
Position Surface form Disambiguated ID Type / Status
Subject Government District (Dallas) E609532 entity
Predicate contains P35 FINISHED
Object Frank Crowley Courts Building
The Frank Crowley Courts Building is a major Dallas County courthouse complex that houses criminal courts and related judicial offices in downtown Dallas, Texas.
E1646307 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: Frank Crowley Courts Building | Statement: [Government District (Dallas), contains, Frank Crowley Courts Building]
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: Frank Crowley Courts Building
Triple: [Government District (Dallas), contains, Frank Crowley Courts Building]
Generated description
The Frank Crowley Courts Building is a major Dallas County courthouse complex that houses criminal courts and related judicial offices in downtown Dallas, Texas.

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_69e2c4d1d3708190a0f2dc6a3a8523bb completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f2aab63f0c8190a459eec33af403de completed April 30, 2026, 1:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100fed4d788190920edc01833203ed completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136992b481909ee04d5c09867f21 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10141161b08190b471a7882a4d8aa0 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 2:32 a.m.