Triple

T33635486
Position Surface form Disambiguated ID Type / Status
Subject Morgan City, Louisiana E861678 entity
Predicate locatedNear P294 FINISHED
Object Berwick, Louisiana
Berwick, Louisiana is a small town in St. Mary Parish in southern Louisiana, situated along the lower Atchafalaya River across from Morgan City.
E2156283 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: Berwick, Louisiana | Statement: [Morgan City, Louisiana, locatedNear, Berwick, Louisiana]
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: Berwick, Louisiana
Triple: [Morgan City, Louisiana, locatedNear, Berwick, Louisiana]
Generated description
Berwick, Louisiana is a small town in St. Mary Parish in southern Louisiana, situated along the lower Atchafalaya River across from Morgan City.

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_69f34981c54c81909b33c3fa2208a52d completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f9726ff081909ae1aba5c2a1c3eb completed May 3, 2026, 7:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a389144051c8190bbbcb40b78ffd733 completed June 22, 2026, 1:35 a.m.
NEDg Description generation batch_6a389253d4f881909a40e2c14b4a6d4e completed June 22, 2026, 1:39 a.m.
NED2 Entity disambiguation (via description) batch_6a38930372408190a387347aba837518 completed June 22, 2026, 1:42 a.m.
Created at: May 1, 2026, 1:42 a.m.