Triple
T1877020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ipanema |
E39167
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Leblon |
E40967
|
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: Leblon | Statement: [Ipanema, borderedBy, Leblon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leblon Context triple: [Ipanema, borderedBy, Leblon]
-
A.
Leblon
chosen
Leblon is an affluent beachfront neighborhood in Rio de Janeiro, Brazil, known for its upscale restaurants, luxury apartments, and vibrant nightlife.
-
B.
Nikolaiviertel
Nikolaiviertel is a historic quarter in central Berlin known for its reconstructed medieval-style streets, traditional German restaurants, and proximity to the Spree River.
-
C.
Kingisepp
Kingisepp is a town in northwestern Russia near the Estonian border, known for its industrial base and historical roots dating back to the 14th century.
-
D.
Holon
Holon is a city in central Israel, part of the Tel Aviv metropolitan area, known for its cultural institutions, museums, and diverse communities.
-
E.
Givatayim
Givatayim is a small, densely populated city in Israel’s Tel Aviv metropolitan area, known for its residential character and proximity to major urban centers.
- 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_69a8862f7074819096afe7fe65e179e9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb0da543481908ab25806e6b80375 |
completed | March 7, 2026, 5 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69addf56e2748190a2044d33d29d8324 |
completed | March 8, 2026, 8:43 p.m. |
Created at: March 4, 2026, 7:34 p.m.