Triple
T24025410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Woman (Death of a Salesman) |
E594941
|
entity |
| Predicate | causesEmotionalImpactOn |
P84862
|
FINISHED |
| Object | Biff Loman |
—
|
NE NERFINISHED |
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: Biff Loman | Statement: [The Woman (Death of a Salesman), causesEmotionalImpactOn, Biff Loman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causesEmotionalImpactOn Context triple: [The Woman (Death of a Salesman), causesEmotionalImpactOn, Biff Loman]
-
A.
emotionalTrigger
chosen
Indicates that one entity causes or elicits an emotional response or reaction in another entity.
-
B.
emotionEffect
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
-
C.
provokesEmotionType
Indicates that one entity causes or elicits a specific type of emotional response in another entity.
-
D.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
-
E.
emotionalChallenge
Indicates a situation where one entity causes or experiences significant emotional difficulty or stress in relation to another entity or circumstance.
- 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_69e288be2c288190a3a46006945557f7 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d76ada608190a9b63d07c2fa90d4 |
completed | April 29, 2026, 10:03 a.m. |
| PD | Predicate disambiguation | batch_69f17639d23c8190bed93434e2f9230a |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:53 p.m.