Triple
T9392517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Slim Pickens |
E226057
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Tonka
Tonka is a 1958 Walt Disney Western film about a young Sioux boy and his horse, set around the events of the Battle of the Little Bighorn.
|
E796329
|
NE FINISHED |
How this triple was built (4 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: Tonka | Statement: [Slim Pickens, notableWork, Tonka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tonka Context triple: [Slim Pickens, notableWork, Tonka]
-
A.
Tinker Toy
Tinker Toy is the nickname of the Douglas A-4 Skyhawk, a compact, carrier-capable attack aircraft used extensively by the U.S. Navy and Marine Corps.
-
B.
Tonk
Tonk is a historic town in the Indian state of Rajasthan, known for its rich Indo-Islamic architectural heritage and cultural significance.
-
C.
Radio Flyer
Radio Flyer is a 1992 American drama-fantasy film about two young brothers who escape their troubled home life through imagination, starring Elijah Wood.
-
D.
Toofer
Toofer is a pretentious, Harvard-educated writer on the fictional sketch show within the TV series "30 Rock."
-
E.
Tec Toy
Tec Toy is a Brazilian electronics and video game company best known for localizing, manufacturing, and popularizing Sega consoles and games in Brazil.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tonka Triple: [Slim Pickens, notableWork, Tonka]
Generated description
Tonka is a 1958 Walt Disney Western film about a young Sioux boy and his horse, set around the events of the Battle of the Little Bighorn.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tonka Target entity description: Tonka is a 1958 Walt Disney Western film about a young Sioux boy and his horse, set around the events of the Battle of the Little Bighorn.
-
A.
Tinker Toy
Tinker Toy is the nickname of the Douglas A-4 Skyhawk, a compact, carrier-capable attack aircraft used extensively by the U.S. Navy and Marine Corps.
-
B.
Tonk
Tonk is a historic town in the Indian state of Rajasthan, known for its rich Indo-Islamic architectural heritage and cultural significance.
-
C.
Radio Flyer
Radio Flyer is a 1992 American drama-fantasy film about two young brothers who escape their troubled home life through imagination, starring Elijah Wood.
-
D.
Toofer
Toofer is a pretentious, Harvard-educated writer on the fictional sketch show within the TV series "30 Rock."
-
E.
Tec Toy
Tec Toy is a Brazilian electronics and video game company best known for localizing, manufacturing, and popularizing Sega consoles and games in Brazil.
- F. None of above. chosen
Provenance (5 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_69ca842f7e3481908bf5bcf52e032dbd |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd510fec6481908b51c497744068c8 |
completed | April 1, 2026, 5:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d10108cd0c8190a38ee2325d3475ce |
completed | April 4, 2026, 12:16 p.m. |
| NEDg | Description generation | batch_69d1028690e48190ad737550f1425028 |
completed | April 4, 2026, 12:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d102f969188190b62fe3b8e7035b1d |
completed | April 4, 2026, 12:24 p.m. |
Created at: March 30, 2026, 7:45 p.m.