Triple
T8714523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirndl |
E206860
|
entity |
| Predicate | apronBowPositionIndicates |
P84043
|
FINISHED |
| Object | marital status of wearer |
—
|
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: marital status of wearer | Statement: [Dirndl, apronBowPositionIndicates, marital status of wearer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: apronBowPositionIndicates Context triple: [Dirndl, apronBowPositionIndicates, marital status of wearer]
-
A.
hasApronType
Indicates that an entity is associated with or characterized by a specific type or category of apron.
-
B.
hasApron
Indicates that one entity possesses or is wearing an apron in relation to another context or entity.
-
C.
positionInArms
Indicates that one entity is being held or carried within the arms of another entity.
-
D.
bearerPosition
Indicates the spatial or positional relationship of a bearer (holder or carrier) relative to the item or entity it bears.
-
E.
hasMilitaryApron
Indicates that a location or facility includes a designated apron area specifically used for military aircraft operations.
- 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_69ca83572d4881909bef3be2b578d539 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5cd6707c819092c9fca34f273d5e |
completed | March 31, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69cc456e806c819087e7d66ee737f242 |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc46c40c54819093d174a4203f9515 |
completed | March 31, 2026, 10:12 p.m. |
Created at: March 30, 2026, 6:35 p.m.