Triple
T19842545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melissa Navia |
E476771
|
entity |
| Predicate | characterRankPortrayed |
P106853
|
FINISHED |
| Object | Lieutenant |
—
|
LITERAL 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: Lieutenant | Statement: [Melissa Navia, characterRankPortrayed, Lieutenant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterRankPortrayed Context triple: [Melissa Navia, characterRankPortrayed, Lieutenant]
-
A.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
-
B.
characterAffiliationPortrayed
chosen
Indicates that a portrayal shows a character as being affiliated with a particular group, organization, or side.
-
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.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
E.
characterPortrayedInYear
Indicates that a particular character was portrayed in a specific year.
- F. None of above.
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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65806375c8190a4f45f14aeb06515 |
completed | April 20, 2026, 4:44 p.m. |
| PD | Predicate disambiguation | batch_69e537e21d2881909b1be82f02b99d40 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:51 p.m.