Triple

T25910615
Position Surface form Disambiguated ID Type / Status
Subject Martin County, North Carolina E652882 entity
Predicate containsSettlement P847 FINISHED
Object Oak City, North Carolina
Oak City, North Carolina is a small rural town in eastern North Carolina known for its agricultural surroundings and close-knit community.
E1331537 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: Oak City, North Carolina | Statement: [Martin County, North Carolina, containsSettlement, Oak City, North Carolina]
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: Oak City, North Carolina
Triple: [Martin County, North Carolina, containsSettlement, Oak City, North Carolina]
Generated description
Oak City, North Carolina is a small rural town in eastern North Carolina known for its agricultural surroundings and close-knit 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_69e7ab3d3f8481909bc53ed64c06af33 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f603c450e88190b6bb85debfac30e4 completed May 2, 2026, 2:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11fe449f6881909fd44c32aa09131d completed May 23, 2026, 7:21 p.m.
NEDg Description generation batch_6a11fef3277c81909157e7d7caa3245b completed May 23, 2026, 7:24 p.m.
NED2 Entity disambiguation (via description) batch_6a11fffd6b1081909ed36e05ffdaed73 completed May 23, 2026, 7:29 p.m.
Created at: April 22, 2026, 8:28 a.m.