Triple
T23568461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanne |
E580033
|
entity |
| Predicate | hasRelationshipTypeWithLukas |
P153198
|
FINISHED |
| Object | romantic interest |
—
|
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 interest | Statement: [Hanne, hasRelationshipTypeWithLukas, romantic interest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithLukas Context triple: [Hanne, hasRelationshipTypeWithLukas, romantic interest]
-
A.
hasRelationshipTypeWithOmar
Indicates that an entity stands in a specified type of interpersonal or associative relationship with Omar.
-
B.
hasRelationshipTypeWithBenBoykewich
Indicates that an entity has a specific type of relationship or connection with Ben Boykewich.
-
C.
hasRelationshipTypeWithEdvarda
Indicates that an entity has a specific type of relationship or connection with Edvarda.
-
D.
hasRelationshipTypeWith Valère
Indicates that an entity stands in a specific, characterized type of relationship with Valère.
-
E.
hasRelationshipTypeWithBalducci
Indicates that there exists a specific, defined type of relationship between an entity and Balducci.
- 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_69e24601a9108190bc31e83833c980e4 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1af6e11008190bdd28c3f85e3004e |
completed | April 29, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69f118bcc0b08190b25a8dddfd461a0e |
completed | April 28, 2026, 8:29 p.m. |
| PDg | Predicate description generation | batch_69f12760784c8190aaeff002ef31febe |
completed | April 28, 2026, 9:32 p.m. |
Created at: April 17, 2026, 6:35 p.m.