Triple
T19454087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Palace Arcade |
E486690
|
entity |
| Predicate | notableGame |
P3198
|
FINISHED |
| Object | Galaga |
—
|
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: Galaga | Statement: [The Palace Arcade, notableGame, Galaga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Galaga Context triple: [The Palace Arcade, notableGame, Galaga]
-
A.
Galaga
chosen
Galaga is a classic fixed-shooter arcade video game from the early 1980s, famous for its alien-swarm attack patterns and addictive high-score gameplay.
-
B.
Zagal
Zagal was an earlier Croatian airline company that operated prior to and was eventually succeeded by Croatia Airlines.
-
C.
Gungi
Gungi is a Wookiee Jedi youngling from the Star Wars universe, known for his distinctive wooden lightsaber and appearances in The Clone Wars.
-
D.
Rootabaga Stories
Rootabaga Stories is a whimsical collection of American fairy tales for children by poet Carl Sandburg, known for its imaginative language and Midwestern folk flavor.
-
E.
The Game Station
The Game Station is a massive orbiting broadcast platform from the Doctor Who universe that hosts deadly reality TV-style contests for human contestants.
- 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_69e633c117ac8190a38c01c3191beaea |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:38 p.m.