Triple
T7591679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Christian Andersen Museum |
E179748
|
entity |
| Predicate | hasChildrenActivities |
P78029
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Hans Christian Andersen Museum, hasChildrenActivities, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChildrenActivities Context triple: [Hans Christian Andersen Museum, hasChildrenActivities, yes]
-
A.
hasChildrenPrograms
Indicates that a program serves as a parent to one or more subordinate or derived programs.
-
B.
hasActivityIn
Indicates that an entity engages in or performs a particular activity within a specified context, location, or domain.
-
C.
hasPrimaryActivities
Indicates that an entity is associated with one or more main activities that define its core functions or operations.
-
D.
hasActivityType
Indicates the specific kind or category of activity associated with an entity or event.
-
E.
hasChildrenWith
Indicates that two entities share one or more biological or adopted children together.
- 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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9b746ac8190b255afdfb9635f72 |
completed | March 27, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e04c2c8190a889d928515d9b8e |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f8184bb08190b2f70545a6aa277c |
completed | March 27, 2026, 9:35 p.m. |
Created at: March 27, 2026, 3:53 p.m.