Triple
T35047030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanjeev and Twinkle |
E1011226
|
entity |
| Predicate | hasContrastingTraits |
P123206
|
FINISHED |
| Object | personality |
—
|
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: personality | Statement: [Sanjeev and Twinkle, hasContrastingTraits, personality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasContrastingTraits Context triple: [Sanjeev and Twinkle, hasContrastingTraits, personality]
-
A.
distinguishingTrait
Indicates that a particular characteristic or feature uniquely differentiates one entity from another.
-
B.
hasContrastingRelationshipWith
Indicates a relationship in which two entities are opposed, divergent, or markedly different in qualities, roles, or effects.
-
C.
providesContrastWith
chosen
Indicates that one entity is used to highlight differences or distinctions when compared with another entity.
-
D.
hasContrastingCharacterType
Indicates that two characters are of distinctly different or opposing character types or archetypes.
-
E.
hasDifferentPersonalityIn
Indicates that an entity exhibits a different personality or character traits within a specified context, setting, or situation.
- 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_69f76dcfdda48190b1ebae5da8b54f12 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:01 p.m.