Triple
T34934878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold Krebs |
E1007537
|
entity |
| Predicate | relationshipToRomance |
P205614
|
FINISHED |
| Object | lack of interest in romantic relationships |
—
|
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: lack of interest in romantic relationships | Statement: [Harold Krebs, relationshipToRomance, lack of interest in romantic relationships]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToRomance Context triple: [Harold Krebs, relationshipToRomance, lack of interest in romantic relationships]
-
A.
relationshipDynamic
Indicates a changing or evolving pattern of interaction between entities, such as shifts in their roles, closeness, or influence over time.
-
B.
sexualRelationshipTo
Indicates that one entity has engaged in a sexual relationship or sexual activity with another entity.
-
C.
relationshipFocus
Indicates a relationship where particular attention, priority, or emphasis is placed on the connection between two or more entities.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
relationshipToLady
Indicates the specific type of social, familial, or personal connection that one entity has to a lady.
- 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_69f76dc513fc819084a1ff52abbfa5bc |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379ff1ba081908eda86acefcf69fb |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4 p.m.