Triple
T35557850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marshall Lee |
E1027549
|
entity |
| Predicate | genderCounterpartOf |
P184056
|
FINISHED |
| Object | Marceline the Vampire Queen |
—
|
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: Marceline the Vampire Queen | Statement: [Marshall Lee, genderCounterpartOf, Marceline the Vampire Queen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderCounterpartOf Context triple: [Marshall Lee, genderCounterpartOf, Marceline the Vampire Queen]
-
A.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
-
B.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
C.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
D.
femaleCounterpartOf
Indicates that one entity is the female equivalent or corresponding counterpart of another entity within a given role, relationship, or category.
-
E.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
- 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_69f76e014fd481909e9f04ac603a2aa9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aaabb58c8190bf81673608ecfb6e |
completed | May 3, 2026, 8:06 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69f7aa6795f481908940838ee7041ff5 |
completed | May 3, 2026, 8:04 p.m. |
Created at: May 3, 2026, 4:04 p.m.