Triple
T30094628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marvin |
E764828
|
entity |
| Predicate | hasMalePartner |
P142829
|
FINISHED |
| Object | Whizzer Brown |
—
|
NE NERFINISHED |
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: Whizzer Brown | Statement: [Marvin, hasMalePartner, Whizzer Brown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMalePartner Context triple: [Marvin, hasMalePartner, Whizzer Brown]
-
A.
hadPartner
Indicates that an entity was in a romantic or life-partner relationship with another entity at some point in time.
-
B.
hadPartnerType
Indicates that an entity was associated with another entity in a specific type or category of partnership.
-
C.
hasBoyfriend
chosen
Indicates that a person is in a romantic relationship where the other party is their boyfriend.
-
D.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
-
E.
hasConcubineFrom
Indicates that a person has a concubine whose origin or affiliation is from a specified place or source.
- 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_69f22474e4288190b5f895fe3974aa92 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ff84df768c81908c65a1a7e33103ad |
completed | May 9, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69ff848d0af881908ee42c27a58af47e |
completed | May 9, 2026, 7:01 p.m. |
Created at: April 29, 2026, 7:07 p.m.