Triple
T11773484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lt. Col. Frank Slade |
E279957
|
entity |
| Predicate | emotionalStateAtEnd |
P76175
|
FINISHED |
| Object | protective and morally engaged |
—
|
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: protective and morally engaged | Statement: [Lt. Col. Frank Slade, emotionalStateAtEnd, protective and morally engaged]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionalStateAtEnd Context triple: [Lt. Col. Frank Slade, emotionalStateAtEnd, protective and morally engaged]
-
A.
statusAtEndOfFilm
Indicates the condition or situation an entity is in when the film concludes.
-
B.
emotionState
chosen
Indicates the emotional condition or feeling that an entity is currently experiencing.
-
C.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
-
D.
intendedEmotion
Indicates the emotion that an action, expression, or communication is meant to evoke in its target, regardless of the actual emotion experienced.
-
E.
emotionEffect
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
- 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_69d6ab01d2688190ad8ed6bda487eaa5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a8c2e8b08190a31b1e284fca2aee |
completed | April 10, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69d8a242cd8c819086ed6c5f292dc8cb |
completed | April 10, 2026, 7:09 a.m. |
Created at: April 8, 2026, 9:41 p.m.