Triple
T20788336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Binz beach promenade |
E511699
|
entity |
| Predicate | hasView |
P854
|
FINISHED |
| Object | Binz pier |
—
|
NE NERFINISHED |
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: Binz pier | Statement: [Binz beach promenade, hasView, Binz pier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Binz pier Context triple: [Binz beach promenade, hasView, Binz pier]
-
A.
Binz
chosen
Binz is a popular seaside resort town on the German island of Rügen, known for its sandy beaches and historic resort architecture.
-
B.
Min Bin
Min Bin was a powerful 16th-century king of the Arakanese Kingdom of Mrauk U, known for expanding its territory and turning it into a major regional maritime power.
-
C.
Pijin
Pijin is an English-based creole language widely used as a lingua franca in the Solomon Islands.
-
D.
Que Banz
Que Banz is a hip-hop artist known for collaborating with Brooklyn rapper Uncle Murda.
-
E.
Zhubin Parang
Zhubin Parang is an American comedy writer and producer best known for his long-running work on The Daily Show, where he rose from staff writer to a key leadership role shaping the program’s satirical voice.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c28dfb8c8190a10289c157a61c67 |
completed | April 21, 2026, 12:19 a.m. |
Created at: April 16, 2026, 12:38 p.m.