Triple

T24530033
Position Surface form Disambiguated ID Type / Status
Subject Gloucester City, New Jersey E606775 entity
Predicate hasHighSchool P113 FINISHED
Object Gloucester City High School
Gloucester City High School is a public secondary school serving students in grades 7–12 from Gloucester City, New Jersey.
E1641823 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: Gloucester City High School | Statement: [Gloucester City, New Jersey, hasHighSchool, Gloucester City High School]
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: Gloucester City High School
Triple: [Gloucester City, New Jersey, hasHighSchool, Gloucester City High School]
Generated description
Gloucester City High School is a public secondary school serving students in grades 7–12 from Gloucester City, New Jersey.

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_69e2c4c90c848190b23c4303620dcaaf completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a89aea408190b2d9b66ff4fd1b46 completed April 30, 2026, 12:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ff856e84081909be6da868a38483d completed May 22, 2026, 6:31 a.m.
NEDg Description generation batch_6a0ff96d423881908d81db0b6bc78921 completed May 22, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0ffa8bb5208190b473d834c40e7ef3 completed May 22, 2026, 6:41 a.m.
Created at: April 18, 2026, 2:25 a.m.