Triple
T5736359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. King Schultz |
E126512
|
entity |
| Predicate | allyOf |
P4662
|
FINISHED |
| Object | Django Freeman |
E126511
|
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: Django Freeman | Statement: [Dr. King Schultz, allyOf, Django Freeman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Django Freeman Context triple: [Dr. King Schultz, allyOf, Django Freeman]
-
A.
Django Freeman
chosen
Django Freeman is a formerly enslaved African-American turned bounty hunter who seeks to rescue his wife in Quentin Tarantino’s Western film "Django Unchained."
-
B.
Sam Freeman
Sam Freeman is a character in the 1977 action film "Grand Theft Auto," which centers on a high-speed cross-country car chase.
-
C.
Ezekiel Jones
Ezekiel Jones is a charming, tech-savvy master thief and one of the main Librarians in the fantasy adventure TV series "The Librarians."
-
D.
Omari Douglas
Omari Douglas is a British actor best known for his breakout role in the acclaimed television drama "It's a Sin."
-
E.
Aubrey Jones
Aubrey Jones was a British Conservative politician who served in senior government roles in the mid-20th century, notably in economic and industrial affairs.
- 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_69c0083082288190b7478cead6b5430a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0255ad6f48190977bf4f037110aa3 |
completed | March 22, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c097f372a08190b4e9d52ba138d872 |
completed | March 23, 2026, 1:31 a.m. |
Created at: March 22, 2026, 3:47 p.m.