Triple
T35871122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peregrini |
E1037224
|
entity |
| Predicate | hasRelationshipTypeWithRabia |
P206081
|
FINISHED |
| Object | romantic 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: romantic relationship | Statement: [Peregrini, hasRelationshipTypeWithRabia, romantic relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithRabia Context triple: [Peregrini, hasRelationshipTypeWithRabia, romantic relationship]
-
A.
hasRelationshipTypeWithOmar
Indicates that an entity stands in a specified type of interpersonal or associative relationship with Omar.
-
B.
hasRelationshipTypeWithAglayaIvanovna
Indicates that an entity has a specific type of relationship or connection with Aglaya Ivanovna.
-
C.
hasRelationshipTypeWithRocky
Indicates that an entity has a specific type of relationship or connection with the entity named Rocky.
-
D.
hasRelationshipTypeWithApu
Indicates that there exists a specific type of relationship between an entity and Apu.
-
E.
hasRelationshipTypeWith Anastasia Steele
Indicates that an entity has a specific type of relationship or relational role with Anastasia Steele.
- 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_69f76e1e701c8190a4990d4978ce4fe6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037ce53de881908cf14141cf3bc570 |
completed | May 12, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:06 p.m.