Triple
T9510759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Meredith |
E229385
|
entity |
| Predicate | hasFictionalCounterpartType |
P58963
|
FINISHED |
| Object | young woman |
—
|
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: young woman | Statement: [Mary Meredith, hasFictionalCounterpartType, young woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalCounterpartType Context triple: [Mary Meredith, hasFictionalCounterpartType, young woman]
-
A.
hasFictionalForm
Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
-
B.
hasFictionalAlterEgoOf
Indicates that one entity is the fictional alter ego, persona, or alternate identity of another entity.
-
C.
hasFictionalUniverseElement
Indicates that one entity is a component, feature, or constituent part of the fictional universe represented by the other entity.
-
D.
hasFictionalProperty
chosen
Indicates that an entity possesses a property, attribute, or characteristic that exists only in a fictional or imaginary context.
-
E.
hasFictionalEstablishmentType
Indicates that an establishment is associated with a particular type or category of fictional setting or institution.
- 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_69ca84777560819084cddd999badc1aa |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9868616c8190856f89fecfa1a02e |
completed | April 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cca567ca448190bf4bcce8ce7dd54f |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:58 p.m.