Triple
T19452087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OS X El Capitan |
E486634
|
entity |
| Predicate | includedApplication |
P1393
|
FINISHED |
| Object | Terminal |
—
|
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: Terminal | Statement: [OS X El Capitan, includedApplication, Terminal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terminal Context triple: [OS X El Capitan, includedApplication, Terminal]
-
A.
Terminal
chosen
Terminal is the built-in command-line interface application for macOS that allows users to interact with the operating system using text-based commands.
-
B.
Terminal
Terminal is a 2018 neo-noir thriller film starring Margot Robbie, known for its stylized visuals and dark, twisting narrative.
-
C.
Terminal
Terminal is a popular multiplayer map in Call of Duty: Modern Warfare 2 set in a modern airport environment featuring tight indoor spaces and open tarmac areas.
-
D.
Terminal C
Terminal C is one of the passenger terminals at Dallas/Fort Worth International Airport, serving various domestic flights and airlines within the airport’s complex.
-
E.
Terminal C
Terminal C is one of the passenger terminals at Hannover Airport in Germany, serving commercial air traffic with check-in, security, and boarding facilities.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63392aab08190aab09b7c356e5b10 |
completed | April 20, 2026, 2:09 p.m. |
Created at: April 10, 2026, 1:38 p.m.