Triple
T34929231
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisa |
E1007379
|
entity |
| Predicate | hasRelationshipTypeWithLuke |
P205613
|
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: [Lisa, hasRelationshipTypeWithLuke, romantic relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithLuke Context triple: [Lisa, hasRelationshipTypeWithLuke, romantic relationship]
-
A.
hasRelationshipTypeWithLukas
Indicates that an entity has a specific type of relationship or connection with Lukas.
-
B.
hasRelationshipTypeWithMerlin
Indicates that an entity has a specific type of relationship or connection with Merlin.
-
C.
hasRelationshipTypeWithCeleste
Indicates that an entity has a specific type of relationship or connection with Celeste.
-
D.
hasRelationshipTypeWith Anastasia Steele
Indicates that an entity has a specific type of relationship or relational role with Anastasia Steele.
-
E.
hasRelationshipTypeWithRocky
Indicates that an entity has a specific type of relationship or connection with the entity named Rocky.
- 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_69f76dc3d83881909d5c3c14455cfa2c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379ff1ba081908eda86acefcf69fb |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4 p.m.