Triple
T16136796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koopa Troop |
E391550
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | Mario franchise |
E406903
|
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: Mario franchise | Statement: [Koopa Troop, appearsIn, Mario franchise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mario franchise Context triple: [Koopa Troop, appearsIn, Mario franchise]
-
A.
Super Mario series
chosen
The Super Mario series is Nintendo’s flagship platform game franchise starring Mario in a wide variety of adventures across imaginative worlds.
-
B.
Mario vs. Donkey Kong series
The Mario vs. Donkey Kong series is a puzzle-platform video game franchise in which Mario navigates intricate, toy-themed levels to outwit Donkey Kong and solve object-based challenges.
-
C.
Mario
Mario is a fictional Italian plumber and the iconic protagonist of Nintendo's long-running Super Mario video game franchise.
-
D.
Mario
Mario is an American R&B singer, songwriter, and occasional actor best known for his early-2000s hits like "Let Me Love You."
-
E.
Mario
Mario is the young, aspiring writer and romantic protagonist of Mario Vargas Llosa’s novel "Aunt Julia and the Scriptwriter."
- 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21a05148c8190bc2b98217fda23cc |
completed | April 17, 2026, 11:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00456d5e74819080c838468ec015ef |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 5:01 a.m.