Triple
T31491108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abstraktes Bild (809-2) |
E803405
|
entity |
| Predicate | hasNoFigurativeSubject |
P202656
|
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: [Abstraktes Bild (809-2), hasNoFigurativeSubject, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoFigurativeSubject Context triple: [Abstraktes Bild (809-2), hasNoFigurativeSubject, true]
-
A.
hasMetaphoricalSubject
Indicates that one entity functions as the metaphorical subject or source domain in a figurative or metaphorical expression involving another entity.
-
B.
hasNoConventionalSubject
Indicates that an action or event occurs without a typical, explicit grammatical subject performing it.
-
C.
hasLiteralMeaning
Indicates that one entity expresses the direct, explicit meaning or sense of another entity (such as a word, phrase, or symbol).
-
D.
hasHumanSubject
Indicates that an entity serves as the human participant or subject involved in an action, event, or relation.
-
E.
hasMetaphoricalContent
Indicates that something contains or expresses meaning through metaphorical, rather than purely literal, content.
- 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_69f348ca04508190ba9379b5329dfd75 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a00a74c0564819081e1c4c29b9d0d15 |
completed | May 10, 2026, 3:42 p.m. |
| PD | Predicate disambiguation | batch_6a00a6a63ef48190a743c88534d9d672 |
completed | May 10, 2026, 3:39 p.m. |
| PDg | Predicate description generation | batch_6a00a74afb3c8190a274bf6681950aa3 |
completed | May 10, 2026, 3:42 p.m. |
Created at: April 30, 2026, 9:38 p.m.