Triple
T2230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City |
E40
|
entity |
| Predicate | hasBorough |
P300
|
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: [New York City, hasBorough, Brooklyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brooklyn Context triple: [New York City, hasBorough, 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.
The Bronx
The Bronx is one of the five boroughs of New York City, known as the birthplace of hip-hop and home to Yankee Stadium and the Bronx Zoo.
-
C.
Manhattan
Manhattan is the densely populated, iconic core borough of New York City, known for its skyscrapers, cultural institutions, and role as a global financial and media center.
-
D.
Queens
Queens is one of the five boroughs of New York City, known for its ethnic diversity, major airports, and mix of residential neighborhoods and commercial centers.
-
E.
New York City
New York City is the largest city in the United States, a global center of finance, culture, media, and technology.
- 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_69a22cde80848190b62c5f556b4d62ba |
completed | Feb. 27, 2026, 11:46 p.m. |
| NER | Named-entity recognition | batch_69a233c52368819093215a9c745f264c |
completed | Feb. 28, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a26c120b98819087a09540c2a57366 |
completed | Feb. 28, 2026, 4:16 a.m. |
Created at: Feb. 27, 2026, 11:48 p.m.