Triple
T4479389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samantha Caine |
E100089
|
entity |
| Predicate | worksWith |
P398
|
FINISHED |
| Object | Mitch Henessey |
E345509
|
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: Mitch Henessey | Statement: [Samantha Caine, worksWith, Mitch Henessey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mitch Henessey Context triple: [Samantha Caine, worksWith, Mitch Henessey]
-
A.
Mitch Henessey
chosen
Mitch Henessey is a wisecracking private investigator and reluctant hero who partners with amnesiac assassin Charly Baltimore in the action film "The Long Kiss Goodnight."
-
B.
Brad Guzan
Brad Guzan is an American professional soccer goalkeeper known for his long MLS career and for representing the United States national team, including at multiple major international tournaments.
-
C.
Douglas Shearer
Douglas Shearer was a pioneering Canadian-American sound engineer and special effects designer in Hollywood, renowned for his groundbreaking work at MGM and multiple Academy Awards.
-
D.
Steven Taylor
Steven Taylor is a companion of the First Doctor in the classic British science fiction television series Doctor Who.
-
E.
Marcus Gerber
Marcus Gerber is a fictional character from the film "Burlesque."
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356db0a008190ad39b68efc095b8d |
completed | March 13, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b62888ea508190a052c0b2edee7fca |
completed | March 15, 2026, 3:33 a.m. |
Created at: March 12, 2026, 11:35 p.m.