Triple
T30924786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Compensation and Reparation Authority |
E787826
|
entity |
| Predicate | typeOfReparation |
P45969
|
FINISHED |
| Object | compensation |
—
|
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: compensation | Statement: [Compensation and Reparation Authority, typeOfReparation, compensation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfReparation Context triple: [Compensation and Reparation Authority, typeOfReparation, compensation]
-
A.
reparationsType
chosen
Indicates the specific category or form of reparations involved in a reparative action or obligation between entities.
-
B.
repairs
Indicates that one entity fixes, restores, or maintains another entity to a proper or functional condition.
-
C.
repairedIn
Indicates that an item or object underwent repair within a specified location or during a particular time period.
-
D.
restorationType
Indicates the specific kind or category of restoration applied to an entity, such as the method, scope, or approach used to return it to a prior or improved state.
-
E.
isRepairable
Indicates that an entity can be restored to proper working condition through repair.
- 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_69f224bfaca88190b9d0dfcc86297fe9 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ff90b673248190b4dda9e005642d17 |
completed | May 9, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69ff8d5bee1081909274052945e98a6f |
completed | May 9, 2026, 7:39 p.m. |
Created at: April 29, 2026, 8:51 p.m.