Triple
T14661110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theodore Twombly |
E344237
|
entity |
| Predicate | emotionalChallenge |
P115245
|
FINISHED |
| Object | difficulty signing divorce papers |
—
|
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: difficulty signing divorce papers | Statement: [Theodore Twombly, emotionalChallenge, difficulty signing divorce papers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionalChallenge Context triple: [Theodore Twombly, emotionalChallenge, difficulty signing divorce papers]
-
A.
emotionalTrigger
Indicates that one entity causes or elicits an emotional response or reaction in another entity.
-
B.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
-
C.
emotionalTrait
Indicates that an entity possesses a particular emotional characteristic, disposition, or affective quality.
-
D.
emotionalTrajectory
Indicates how an entity’s emotional state changes or progresses over time in relation to another entity or context.
-
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. chosen
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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51c84448190a1f5fa9ab8a1e2c4 |
completed | April 14, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69de6576f0208190aa94d995e797ac38 |
completed | April 14, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69de716c17cc8190aeb85296abee85a7 |
completed | April 14, 2026, 4:55 p.m. |
Created at: April 10, 2026, 1:27 a.m.