Triple
T27938648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Hogancamp's backyard town |
E700680
|
entity |
| Predicate | hasFictionalName |
P83029
|
FINISHED |
| Object | Marwencol |
—
|
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: Marwencol | Statement: [Mark Hogancamp's backyard town, hasFictionalName, Marwencol]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalName Context triple: [Mark Hogancamp's backyard town, hasFictionalName, Marwencol]
-
A.
hasFictionalAlias
chosen
Indicates that an entity is known by an alternative name or identity within a fictional context.
-
B.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
C.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
-
D.
hasFictionalForm
Indicates that an entity has a counterpart or representation that exists within a fictional or imaginary context.
-
E.
hasFictionalProperty
Indicates that an entity possesses a property, attribute, or characteristic that exists only in a fictional or imaginary context.
- 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_69ef6a5028108190a14696d9821dde49 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fccdd496048190bca801a8a9eecb62 |
completed | May 7, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
Created at: April 27, 2026, 7:15 p.m.