Triple
T22479656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tighina |
E555729
|
entity |
| Predicate | nativeName |
P15
|
FINISHED |
| Object | Bender |
—
|
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: Bender | Statement: [Tighina, nativeName, Bender]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bender Context triple: [Tighina, nativeName, Bender]
-
A.
Bender
Bender is a common German-origin surname borne by various notable individuals across fields such as entertainment, sports, and politics.
-
B.
Bender
Bender was a Hall of Fame Major League Baseball pitcher from the early 20th century, best known for his success with the Philadelphia Athletics.
-
C.
Bender
chosen
Bender is a historic city in eastern Moldova, known for its strategic location on the Dniester River and its prominent fortress.
-
D.
Bender Bending Rodríguez
Bender Bending Rodríguez is a hard-drinking, foul-mouthed robot from the animated series Futurama, known for his selfish antics, dark humor, and occasional moments of unexpected loyalty.
-
E.
BenderSpink
BenderSpink was an American film and television production company known for developing and producing a range of Hollywood genre and comedy projects.
- 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_69e11e53897c819088863779f8c50bb0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15c3836a08190b6f0d88b94cb80a3 |
completed | April 29, 2026, 1:17 a.m. |
Created at: April 16, 2026, 8:49 p.m.