Triple
T10632407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Kincaid |
E250489
|
entity |
| Predicate | settingOfLoveAffair |
P95083
|
FINISHED |
| Object | Iowa farm |
—
|
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: Iowa farm | Statement: [Robert Kincaid, settingOfLoveAffair, Iowa farm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfLoveAffair Context triple: [Robert Kincaid, settingOfLoveAffair, Iowa farm]
-
A.
loveInterestPortrayedBy
Indicates that a character’s romantic interest is depicted or played by a particular actor or performer.
-
B.
romanticTriangleInvolves
Indicates a romantic relationship structure in which three individuals are mutually or asymmetrically involved in overlapping romantic connections.
-
C.
settingOfTemptation
Indicates the location or context in which a temptation occurs or is experienced.
-
D.
romanticArc
Indicates a developing or ongoing romantic relationship or storyline between the involved entities.
-
E.
settingOfConflict
Indicates the location or context in which a conflict between entities takes place.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df95f5e88190b34ce3ec972759ef |
completed | April 8, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69d6dd83b114819098e84dc658e82d7e |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df463ea8819091d6683e476b4f21 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 9:02 p.m.