Triple
T32129368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elliot Stabler – Christopher Meloni |
E820597
|
entity |
| Predicate | characterTraitsEmphasized |
P37384
|
FINISHED |
| Object | moral conviction |
—
|
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: moral conviction | Statement: [Elliot Stabler – Christopher Meloni, characterTraitsEmphasized, moral conviction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterTraitsEmphasized Context triple: [Elliot Stabler – Christopher Meloni, characterTraitsEmphasized, moral conviction]
-
A.
associatedCharacterTrait
chosen
Indicates a relationship where a character is linked to, or described by, a particular trait or quality.
-
B.
childCharacterTrait
Indicates that a child possesses or exhibits a particular character trait.
-
C.
secondaryCharacterTrait
Indicates that a secondary or supporting character possesses a particular attribute, quality, or personality trait.
-
D.
protagonistCharacteristic
Indicates that a characteristic, trait, or defining quality is attributed to the protagonist in a narrative or scenario.
-
E.
agingCharacteristics
Indicates how the qualities, properties, or behavior of something change as it ages or over time.
- 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_69f34902d42c819083a8e6bba9a8bb9a |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69febce5877c8190a5e000ef5331ec88 |
completed | May 9, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_69febad1cd588190abc7686bcb39a371 |
completed | May 9, 2026, 4:40 a.m. |
Created at: May 1, 2026, 12:29 a.m.