Triple
T15186182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Genesis Rodriguez |
E362880
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Tusk |
E389493
|
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: Tusk | Statement: [Genesis Rodriguez, notableWork, Tusk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tusk Context triple: [Genesis Rodriguez, notableWork, Tusk]
-
A.
Tusk
Tusk is a Polish surname most prominently associated with Donald Tusk, a leading Polish and European Union politician.
-
B.
Tusk
Tusk is the live Russian boar mascot that represents the University of Arkansas Razorbacks football team at games and events.
-
C.
Tusk
chosen
Tusk is a 2014 horror-comedy film written and directed by Kevin Smith about a podcaster who is grotesquely transformed into a walrus by a deranged seafarer.
-
D.
Komo
The Komo are an ethnic group indigenous to western Ethiopia, particularly associated with the Gambela Region, with their own distinct language and cultural traditions.
-
E.
Komo
Komo is a town located in Hela Province in the Highlands region of Papua New Guinea.
- 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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0067995fc8190b048f15086bd42f0 |
completed | April 15, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed32e425c819083f10f947c258a9b |
completed | May 9, 2026, 6:24 a.m. |
Created at: April 10, 2026, 3:09 a.m.