Triple
T21445359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Stephen Fleming |
E529057
|
entity |
| Predicate | affairCharacteristics |
P144373
|
FINISHED |
| Object | obsessive |
—
|
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: obsessive | Statement: [Dr. Stephen Fleming, affairCharacteristics, obsessive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affairCharacteristics Context triple: [Dr. Stephen Fleming, affairCharacteristics, obsessive]
-
A.
featuresExtramaritalAffair
Indicates that one entity is involved in or depicts a romantic or sexual relationship occurring outside of a committed partnership or marriage.
-
B.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
-
C.
settingOfLoveAffair
Indicates the location or environment in which a love affair takes place.
-
D.
marriageCharacterization
Indicates how a marriage is described, evaluated, or characterized in terms of its qualities, dynamics, or nature.
-
E.
hasMaritalInfidelitySubplot
Indicates that the work includes a subplot involving a character engaging in romantic or sexual infidelity within a marriage.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b707ecd88190b3576b8923840870 |
completed | April 22, 2026, 11:54 a.m. |
| PD | Predicate disambiguation | batch_69e631df1b38819088d3604854e697b4 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e63d2aca38819094d312078feaa436 |
completed | April 20, 2026, 2:50 p.m. |
Created at: April 16, 2026, 6:05 p.m.