Triple
T15284157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aunt Elizabeth |
E365349
|
entity |
| Predicate | complicates |
P18423
|
FINISHED |
| Object | romantic relationship between David and Susan |
—
|
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: romantic relationship between David and Susan | Statement: [Aunt Elizabeth, complicates, romantic relationship between David and Susan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: complicates Context triple: [Aunt Elizabeth, complicates, romantic relationship between David and Susan]
-
A.
complication
chosen
Indicates that one event, action, or condition makes another more difficult, problematic, or introduces an additional obstacle or entanglement in the situation.
-
B.
hasComplicatedRelationshipWith
Indicates that one entity is involved in a complex, often ambiguous or difficult-to-define interpersonal or relational dynamic with another entity.
-
C.
comprende
Indicates that one entity includes, contains, or encompasses another as a part or component.
-
D.
complicitIn
Indicates involvement in or knowing participation in a wrongful, illegal, or unethical act carried out by another party.
-
E.
distorts
Indicates that one entity alters another in a way that changes, warps, or misrepresents its original form, appearance, or meaning.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e53c9588190a6cb61ac8805c706 |
completed | April 15, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69deca90739081909bd1b797cdb8af2b |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:15 a.m.