Triple
T15436081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iqta system |
E369765
|
entity |
| Predicate | compensates |
P72053
|
FINISHED |
| Object | service instead of cash salary |
—
|
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: service instead of cash salary | Statement: [Iqta system, compensates, service instead of cash salary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: compensates Context triple: [Iqta system, compensates, service instead of cash salary]
-
A.
compensated
Indicates that one entity provides payment or some form of recompense to another entity in return for goods, services, or loss incurred.
-
B.
compensationMechanism
chosen
Indicates a relationship where one entity provides payment, benefits, or other forms of recompense to another in return for a loss, service, or obligation.
-
C.
compensationModel
Indicates the type or structure of payment or rewards provided in exchange for work, services, or performance.
-
D.
complement
Indicates that one entity completes, enhances, or makes another entity whole or more effective by providing what it lacks.
-
E.
complements
Indicates that one entity enhances, completes, or improves another by providing qualities or functions that fit well together.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03edb3ec481908b26164d4470c9bc |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded27f45548190a6d2b1b85cb47444 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:21 a.m.