Triple
T10950668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamburg Airport |
E258716
|
entity |
| Predicate | has category |
P82611
|
FINISHED |
| Object | major German airport |
—
|
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: major German airport | Statement: [Hamburg Airport, has category, major German airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: has category Context triple: [Hamburg Airport, has category, major German airport]
-
A.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
B.
containsCategory
Indicates that one entity includes or encompasses a specific category as part of its classification or organizational structure.
-
C.
hasCategories
chosen
Indicates that an entity is associated with one or more categories that classify or group it.
-
D.
hasCategoryWithin
Indicates that one category is contained within or is a subcategory of another category.
-
E.
haveCategoryCode
Indicates that an entity is associated with a specific classification or category identified by a code.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770ed2f1c819081ec58457f57889d |
completed | April 9, 2026, 9:27 a.m. |
| PD | Predicate disambiguation | batch_69d72e874f48819096ffa878f90c7d5b |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.