Triple

T34734169
Position Surface form Disambiguated ID Type / Status
Subject Congo Square E1001290 entity
Predicate hasNameInPast P199075 FINISHED
Object Place Congo
Place Congo was the historical name for New Orleans’ Congo Square, a public gathering place famous for its vibrant African and Afro-Creole music, dance, and cultural traditions during the 18th and 19th centuries.
E2109621 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: Place Congo | Statement: [Congo Square, hasNameInPast, Place Congo]
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: Place Congo
Triple: [Congo Square, hasNameInPast, Place Congo]
Generated description
Place Congo was the historical name for New Orleans’ Congo Square, a public gathering place famous for its vibrant African and Afro-Creole music, dance, and cultural traditions during the 18th and 19th centuries.

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_69f76daf739881909ed3554f98a2b433 completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69ff1dfd700c8190bf23463b790c85d1 completed May 9, 2026, 11:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a375befa54c81909a986c2081739681 completed June 21, 2026, 3:35 a.m.
NEDg Description generation batch_6a375ce7a72c819098f1ff70b5e9dc2f completed June 21, 2026, 3:39 a.m.
NED2 Entity disambiguation (via description) batch_6a375d775e748190847a041f568b50d8 completed June 21, 2026, 3:41 a.m.
Created at: May 3, 2026, 3:59 p.m.