Triple
T37853402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Leeds |
E944116
|
entity |
| Predicate | relationshipTypeWithSarahNorman |
P204427
|
FINISHED |
| Object | complex emotional relationship |
—
|
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: complex emotional relationship | Statement: [James Leeds, relationshipTypeWithSarahNorman, complex emotional relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithSarahNorman Context triple: [James Leeds, relationshipTypeWithSarahNorman, complex emotional relationship]
-
A.
relationshipDynamicWithSarahBrown
Indicates a changing or evolving interpersonal relationship involving Sarah Brown.
-
B.
relationshipToSarahLeary
Indicates the specific type of relationship or connection an entity has to Sarah Leary.
-
C.
relationshipToNormaDesmond
Indicates the specific interpersonal or social connection an entity has with Norma Desmond.
-
D.
hasRelationshipTypeWith Alexandra Bergson
Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
-
E.
relationshipTypeWithNinaSayers
Indicates the specific nature or category of relationship that an entity has with Nina Sayers.
- 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_69f76eed4d9c81908b1b71ba9e3b61fe |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.