Triple

T28943268
Position Surface form Disambiguated ID Type / Status
Subject Saint Mary’s Cemetery, Greenwich E730510 entity
Predicate hasNameInEnglish P3437 FINISHED
Object Saint Mary’s Cemetery
Saint Mary’s Cemetery is a burial ground located in Greenwich, Connecticut, serving as a historic Catholic cemetery for the local community.
 E1912311 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: Saint Mary’s Cemetery | Statement: [Saint Mary’s Cemetery, Greenwich, hasNameInEnglish, Saint Mary’s Cemetery]
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: Saint Mary’s Cemetery
Triple: [Saint Mary’s Cemetery, Greenwich, hasNameInEnglish, Saint Mary’s Cemetery]
Generated description
Saint Mary’s Cemetery is a burial ground located in Greenwich, Connecticut, serving as a historic Catholic cemetery for the local community.

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_69f043ea0aa88190a25acbf46157995a completed April 28, 2026, 5:21 a.m.
NER Named-entity recognition batch_69f65b84a0cc819088b1758fd9dcf258 completed May 2, 2026, 8:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2789180a8081909ece1a5efc079764 completed June 9, 2026, 3:31 a.m.
NEDg Description generation batch_6a278a1d4c0881909d4e6ae051872ba5 completed June 9, 2026, 3:35 a.m.
NED2 Entity disambiguation (via description) batch_6a278b7daf3c819090c29e305656692d completed June 9, 2026, 3:41 a.m.
Created at: April 28, 2026, 8:38 a.m.