Triple
T35176147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miriam Leivers |
E1015706
|
entity |
| Predicate | relationshipTypeWithPaulMorel |
P95781
|
FINISHED |
| Object | romantic but unconsummated for much of the novel |
—
|
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 but unconsummated for much of the novel | Statement: [Miriam Leivers, relationshipTypeWithPaulMorel, romantic but unconsummated for much of the novel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithPaulMorel Context triple: [Miriam Leivers, relationshipTypeWithPaulMorel, romantic but unconsummated for much of the novel]
-
A.
relationshipToPaul
chosen
Indicates a specified type of personal or social relationship that an entity has with Paul.
-
B.
relationshipStatusWithPaulMontague
Indicates the nature or state of an entity’s personal relationship with Paul Montague.
-
C.
hasRelationshipTypeWith Philippe Renaldo
Indicates that there exists a specific type or category of relationship between an entity and Philippe Renaldo.
-
D.
relationshipToAnnaPaul
Indicates that one entity has a specified personal or social relationship to Anna Paul.
-
E.
relationshipToPavelVlasov
Indicates the nature or type of relationship an entity has with Pavel Vlasov.
- 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_69f76ddcc108819097f96853b7ed9ef4 |
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, 4:02 p.m.