Triple
T28340780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stuart Pritchard |
E717804
|
entity |
| Predicate | romanticGoal |
P114366
|
FINISHED |
| Object | to date attractive women in Los Angeles |
—
|
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: to date attractive women in Los Angeles | Statement: [Stuart Pritchard, romanticGoal, to date attractive women in Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanticGoal Context triple: [Stuart Pritchard, romanticGoal, to date attractive women in Los Angeles]
-
A.
relationshipGoal
chosen
Indicates that one entity has a desired or intended relationship status or type involving another entity.
-
B.
romanticFeeling
Indicates that one entity experiences romantic attraction or affection toward another entity.
-
C.
romanticOutcome
Indicates that a romantic relationship or interaction between entities results in a particular outcome, such as success, failure, or change in status.
-
D.
romanticArc
Indicates a developing or ongoing romantic relationship or storyline between the involved entities.
-
E.
romanticPattern
Indicates a recurring style, tendency, or structure in how romantic relationships or attractions develop or are expressed between entities.
- 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_69eff6eb30388190b898b96c4be6f49d |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a931748190a637e631a52bbfaa |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 28, 2026, 12:39 a.m.