Triple
T7071091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meredith Black |
E164696
|
entity |
| Predicate | emotionallyAffectedBy |
P40524
|
FINISHED |
| Object | Walter Black's use of the beaver puppet |
—
|
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: Walter Black's use of the beaver puppet | Statement: [Meredith Black, emotionallyAffectedBy, Walter Black's use of the beaver puppet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emotionallyAffectedBy Context triple: [Meredith Black, emotionallyAffectedBy, Walter Black's use of the beaver puppet]
-
A.
emotionEffect
Indicates that one entity’s emotional state causes or influences a change in another entity’s feelings, behavior, or condition.
-
B.
emotionallyAttachedTo
Indicates that one entity has a strong emotional bond, affection, or dependence directed toward another entity.
-
C.
emotionalDynamic
Indicates how emotions, moods, or affective states change, interact, or influence each other between entities over time.
-
D.
affectedPerson
chosen
Indicates that a particular person is impacted or influenced by an event, action, or condition.
-
E.
feltIn
Indicates that a sensation, emotion, or effect is experienced within a particular location, context, or entity.
- 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_69c6887b96548190a8a9b3ac8adf4119 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4c862f481908d1faf6ed57774f1 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bfcb948190a5ada74fb8c054cb |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:39 p.m.