Triple
T9462319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Die kleine Stadt |
E228177
|
entity |
| Predicate | hasAuthorCharacteristic |
P89076
|
FINISHED |
| Object | blend of satire and psychological analysis |
—
|
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: blend of satire and psychological analysis | Statement: [Die kleine Stadt, hasAuthorCharacteristic, blend of satire and psychological analysis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorCharacteristic Context triple: [Die kleine Stadt, hasAuthorCharacteristic, blend of satire and psychological analysis]
-
A.
hasAuthor
Indicates that an entity is written or created by a specific author.
-
B.
hasRoleCharacteristic
Indicates that an entity possesses a specific characteristic, quality, or attribute associated with a particular role.
-
C.
hasPerformerCharacteristic
Indicates that a performer possesses a particular attribute, quality, or characteristic.
-
D.
hasAuthorType
Indicates that an entity is associated with an author characterized by a specific role, category, or type.
-
E.
hasAuthorBackgroundIn
Indicates that an author possesses a particular background, such as education, experience, or expertise, in a specified field or domain.
- 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_69ca846fee388190a6ec273fd644b88b |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fcd9794819093c392489d4efbe9 |
completed | April 1, 2026, 8:27 p.m. |
| PD | Predicate disambiguation | batch_69cca55caaa8819089c5138e014892d3 |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69ccbf9b080c819098934a18cf2bac5d |
completed | April 1, 2026, 6:47 a.m. |
Created at: March 30, 2026, 7:53 p.m.