Triple

T27685853
Position Surface form Disambiguated ID Type / Status
Subject Pennsylvania Reserve Division E698029 entity
Predicate hasPart P35 FINISHED
Object 13th Pennsylvania Reserves
The 13th Pennsylvania Reserves was a Union infantry regiment from Pennsylvania that served in the American Civil War as part of the state’s Reserve Corps.
E1817019 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: 13th Pennsylvania Reserves | Statement: [Pennsylvania Reserve Division, hasPart, 13th Pennsylvania Reserves]
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: 13th Pennsylvania Reserves
Triple: [Pennsylvania Reserve Division, hasPart, 13th Pennsylvania Reserves]
Generated description
The 13th Pennsylvania Reserves was a Union infantry regiment from Pennsylvania that served in the American Civil War as part of the state’s Reserve Corps.

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_69ef590df8708190af5488f0638e790c completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f63572ae688190b6529409b47e1ce8 completed May 2, 2026, 5:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1632dc42088190b810f28bed58a14d completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a1634c284bc8190a09d836655486dc7 completed May 27, 2026, 12:03 a.m.
NED2 Entity disambiguation (via description) batch_6a1638ab3f5c8190be17ee9121039cd1 completed May 27, 2026, 12:19 a.m.
Created at: April 27, 2026, 2:49 p.m.