Triple
T21267553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lilli Vanessi |
E524166
|
entity |
| Predicate | relationshipTypeWithFredGraham |
P143434
|
FINISHED |
| Object | love-hate 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: love-hate relationship | Statement: [Lilli Vanessi, relationshipTypeWithFredGraham, love-hate relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithFredGraham Context triple: [Lilli Vanessi, relationshipTypeWithFredGraham, love-hate relationship]
-
A.
hasRelationshipTypeWith Tai Frasier
Indicates that there exists a specific type of relationship between an entity and Tai Frasier.
-
B.
relationshipTypeWithFrankUnderwood
Indicates the specific nature or category of relationship that an entity has with Frank Underwood.
-
C.
relationshipTypeWith Francesca Johnson
Indicates the specific nature or category of the relationship that an entity has with Francesca Johnson.
-
D.
hasRelationshipTypeWithFreddieThornhill
Indicates that an entity has a specific type of interpersonal relationship with Freddie Thornhill.
-
E.
relationshipTypeWith Eugene Gant
Indicates the specific nature or category of relationship that an entity has with Eugene Gant.
- 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_69e0b5156d7881909bd4f83676590715 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e735ee38cc81909b4e7c5996bb64f9 |
completed | April 21, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69e5f6161dac8190b06009cd180e3ff7 |
completed | April 20, 2026, 9:47 a.m. |
| PDg | Predicate description generation | batch_69e5f9943ed881909ef49045c5bcf6df |
completed | April 20, 2026, 10:01 a.m. |
Created at: April 16, 2026, 4 p.m.