Triple

T32511969
Position Surface form Disambiguated ID Type / Status
Subject Fall River waterfront E830956 entity
Predicate isAccessibleFrom P1985 FINISHED
Object downtown Fall River
Downtown Fall River is the central urban district of Fall River, Massachusetts, known for its historic architecture, commercial activity, and proximity to the city’s waterfront.
 E830956 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: downtown Fall River | Statement: [Fall River waterfront, isAccessibleFrom, downtown Fall River]
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: downtown Fall River
Triple: [Fall River waterfront, isAccessibleFrom, downtown Fall River]
Generated description
Downtown Fall River is the central urban district of Fall River, Massachusetts, known for its historic architecture, commercial activity, and proximity to the city’s waterfront.

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_69f3492318348190ba37fb6b5f1d67f4 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c49aa3e08190bb13ff57b2878c5d completed May 3, 2026, 3:44 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3470626c6c8190acd20484c4f9c6a1 completed June 18, 2026, 10:25 p.m.
NEDg Description generation batch_6a34710734988190a0a6880097a6a639 completed June 18, 2026, 10:28 p.m.
NED2 Entity disambiguation (via description) batch_6a3471ce69508190bbd47938ea429317 completed June 18, 2026, 10:31 p.m.
Created at: May 1, 2026, 1 a.m.