Triple
T21416265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Papa Johns |
E528302
|
entity |
| Predicate | competitor |
P1375
|
FINISHED |
| Object | Pizza Hut |
—
|
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: Pizza Hut | Statement: [Papa Johns, competitor, Pizza Hut]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pizza Hut Context triple: [Papa Johns, competitor, Pizza Hut]
-
A.
Pizza Hut
chosen
Pizza Hut is a global American restaurant chain known for its pizza, pasta, and other Italian-American dishes, operating thousands of locations worldwide.
-
B.
Domino's Pizza
Domino's Pizza is a global pizza delivery and carryout restaurant chain known for its extensive franchise network and emphasis on quick, convenient service.
-
C.
Papa Johns
Papa Johns is a major American pizza restaurant franchise known for its delivery and carryout services worldwide.
-
D.
Pizza King
Pizza King is a local pizzeria and restaurant operating in Lawndale.
-
E.
Pizza Planet
Pizza Planet is the fictional, space-themed pizza restaurant and arcade from the Toy Story franchise, known for its alien mascots and claw machine game.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b205d17c8190b9b6b5708658f9be |
completed | April 22, 2026, 11:33 a.m. |
Created at: April 16, 2026, 5:46 p.m.