Triple
T34976056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krakozhian |
E1008679
|
entity |
| Predicate | hasNotablePortrayalBy |
P143408
|
FINISHED |
| Object | Tom Hanks |
E10383
|
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: Tom Hanks | Statement: [Krakozhian, hasNotablePortrayalBy, Tom Hanks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotablePortrayalBy Context triple: [Krakozhian, hasNotablePortrayalBy, Tom Hanks]
-
A.
hasYoungPortrayalOf
Indicates that one entity is a portrayal or depiction of another entity specifically in their younger age or earlier life stage.
-
B.
hasPortrayedRole
Indicates that an entity has performed or depicted a specific role or character, typically in a work such as a film, play, or television show.
-
C.
hasPortrayedPersonRole
Indicates that an entity has performed or held a specific role in portraying a particular person (e.g., in a film, play, or other representation).
-
D.
notableCharacterPortrayal
chosen
Indicates that an entity is recognized for its portrayal or depiction of a particular character, typically in a performance or narrative work.
-
E.
hasNotablePortrayerOccupation
Indicates that the occupation specified is a notable profession of a person who portrays the given entity (such as an actor playing a character).
- F. None of above.
Provenance (4 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_69f76dc78a308190a1ac29ad4a9a4895 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0357f9670081908a7ba1cd46a0b46a |
completed | May 12, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a37b26eb49081908a7610f03ca6b975 |
completed | June 21, 2026, 9:44 a.m. |
| PD | Predicate disambiguation | batch_6a03575e3258819093303248d1569f95 |
completed | May 12, 2026, 4:37 p.m. |
Created at: May 3, 2026, 4:01 p.m.