Triple
T34004611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pijpelijntjes |
E871923
|
entity |
| Predicate | hasAuthorRealLifeParallels |
P93832
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Pijpelijntjes, hasAuthorRealLifeParallels, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorRealLifeParallels Context triple: [Pijpelijntjes, hasAuthorRealLifeParallels, true]
-
A.
hasFictionalAutobiographer
Indicates that an entity is associated with a fictional character who serves as its autobiographical narrator or self-describing author within a narrative.
-
B.
hasFictionalAuthor
Indicates that one entity is the fictional or in-universe author of a work attributed to them.
-
C.
basedOnRealLife
chosen
Indicates that something is derived from, inspired by, or directly adapted from actual real-world events, people, or situations.
-
D.
hasAuthorRealName
Indicates that an entity (such as a work or pseudonym) is associated with the actual, legal name of its author.
-
E.
hasRealityCounterpartInFiction
Indicates that a fictional element corresponds to or is based on a real-world counterpart within a work of fiction.
- 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_69f349a08848819084b348d64c1879c3 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd0d0ba5c48190bddb3f0e6637544c |
completed | May 7, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69fd0c4324a8819086c90adf46216e0e |
completed | May 7, 2026, 10:03 p.m. |
Created at: May 1, 2026, 1:50 a.m.