Triple
T12848020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rudd Concession |
E307233
|
entity |
| Predicate | compensationIncludes |
P107203
|
FINISHED |
| Object | monthly payment in gold |
—
|
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: monthly payment in gold | Statement: [Rudd Concession, compensationIncludes, monthly payment in gold]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: compensationIncludes Context triple: [Rudd Concession, compensationIncludes, monthly payment in gold]
-
A.
compensationCategory
Indicates the type or classification of compensation associated with an entity, such as how or in what form payment or remuneration is provided.
-
B.
compensationPolicy
Indicates the rules or guidelines that govern how compensation (such as salary, bonuses, or benefits) is determined and provided.
-
C.
compensationModel
Indicates the type or structure of payment or rewards provided in exchange for work, services, or performance.
-
D.
compensated
Indicates that one entity provides payment or some form of recompense to another entity in return for goods, services, or loss incurred.
-
E.
compensationRate
Indicates the rate or amount of payment provided in exchange for a specified unit of work, time, or service.
- 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_69d7bdf5e7cc8190be357278bc5ba3bb |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714208f881908f7f8a921362909a |
completed | April 10, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69d96fa3002881908000357b1f95a3ac |
completed | April 10, 2026, 9:46 p.m. |
| PDg | Predicate description generation | batch_69d9713e45a88190acd346f066093550 |
completed | April 10, 2026, 9:53 p.m. |
Created at: April 9, 2026, 5:36 p.m.