Triple
T13322714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishop of Brooklyn |
E317352
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Brooklyn |
E5446
|
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: Brooklyn | Statement: [Bishop of Brooklyn, city, Brooklyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brooklyn Context triple: [Bishop of Brooklyn, city, Brooklyn]
-
A.
Brooklyn
chosen
Brooklyn is a populous and culturally diverse borough of New York City known for its distinct neighborhoods, arts scene, and iconic landmarks like the Brooklyn Bridge.
-
B.
Brooklyn
Brooklyn is a small inner-ring suburb of Cleveland located in Cuyahoga County, Ohio.
-
C.
Brooklyn
Brooklyn is a residential suburb within the Milnerton area of Cape Town, South Africa.
-
D.
Brooklyn
Brooklyn is a small city in Poweshiek County, Iowa, known for its collection of flags from around the world and its nickname "Community of Flags."
-
E.
Brooklyn
"Brooklyn" is a 2015 period drama film about a young Irish woman who emigrates to New York in the 1950s and must choose between her new life in America and her roots in Ireland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9992ab83c8190982d9f54dff6919f |
completed | April 11, 2026, 12:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73053a1888190a234e8c119a4202a |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 9, 2026, 9:30 p.m.