Triple
T14933337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sue Bierman Park |
E372326
|
entity |
| Predicate | hasView |
P854
|
FINISHED |
| Object | Embarcadero |
E38238
|
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: Embarcadero | Statement: [Sue Bierman Park, hasView, Embarcadero]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Embarcadero Context triple: [Sue Bierman Park, hasView, Embarcadero]
-
A.
Embarcadero
chosen
Embarcadero is a historic waterfront district in San Francisco known for its piers, ferry terminal, and scenic promenade along the bay.
-
B.
Embarcadero Technologies
Embarcadero Technologies is a software company best known for developing database tools and the Delphi rapid application development environment for Windows and cross-platform applications.
-
C.
Delphi
Delphi is a central antagonist in the stage play "Harry Potter and the Cursed Child," portrayed as a mysterious young witch with a powerful and dangerous connection to Voldemort.
-
D.
Delphi
Delphi is an ancient Greek sanctuary and archaeological site famed for the Oracle of Apollo and its central role in classical Greek religion and culture.
-
E.
Borland
Borland was a prominent software company best known for its influential development tools and programming environments, particularly during the 1980s and 1990s.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded646a0808190ba5c0c91bde011c5 |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e8ac6d08190809045a6d00a3d47 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:37 a.m.