Triple
T32654846
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brooklyn Community Board 1 |
E834832
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
CB1 Brooklyn
CB1 Brooklyn is the New York City community board that represents and advises on local issues in the northern Brooklyn neighborhoods of Greenpoint and Williamsburg.
|
E2016795
|
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: CB1 Brooklyn | Statement: [Brooklyn Community Board 1, hasAbbreviation, CB1 Brooklyn]
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: CB1 Brooklyn Triple: [Brooklyn Community Board 1, hasAbbreviation, CB1 Brooklyn]
Generated description
CB1 Brooklyn is the New York City community board that represents and advises on local issues in the northern Brooklyn neighborhoods of Greenpoint and Williamsburg.
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_69f3492f72248190ba42fa596aea50e1 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6c77a3984819095c21ae913206e94 |
completed | May 3, 2026, 3:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3492ad7a488190b499fc491a1fbc6c |
completed | June 19, 2026, 12:51 a.m. |
| NEDg | Description generation | batch_6a34938a58dc8190ab8e23d0b021db8b |
completed | June 19, 2026, 12:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a34941dcdbc8190b6549f9be8eb672f |
completed | June 19, 2026, 12:58 a.m. |
Created at: May 1, 2026, 1:08 a.m.