Triple
T38431830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Natalie Strout |
E903822
|
entity |
| Predicate | relationshipTypeWithFrankFowler |
P204651
|
FINISHED |
| Object | romantic affair |
—
|
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 affair | Statement: [Natalie Strout, relationshipTypeWithFrankFowler, romantic affair]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithFrankFowler Context triple: [Natalie Strout, relationshipTypeWithFrankFowler, romantic affair]
-
A.
relationshipTypeWithFrankUnderwood
Indicates the specific nature or category of relationship that an entity has with Frank Underwood.
-
B.
relationshipTypeWithFrasierCrane
Indicates the specific type or nature of relationship an entity has with Frasier Crane.
-
C.
relationshipTypeWith Francesca Johnson
Indicates the specific nature or category of the relationship that an entity has with Francesca Johnson.
-
D.
hasRelationshipTypeWith Fran Fine
Indicates that an entity is connected to Fran Fine by a specific, categorized type of relationship (e.g., familial, professional, romantic, or social).
-
E.
relationshipTypeWithFredGraham
Indicates the specific nature or category of relationship that an entity has with Fred Graham.
- 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_69f76e6a2024819081aa04f4932f89d2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:31 p.m.