Triple
T12924862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O-1 |
E309216
|
entity |
| Predicate | payCategory |
P107043
|
FINISHED |
| Object | officer |
—
|
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: officer | Statement: [O-1, payCategory, officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: payCategory Context triple: [O-1, payCategory, officer]
-
A.
payType
Indicates the method or category of payment used in a transaction or compensation arrangement.
-
B.
pays
Indicates that one entity gives money or another form of compensation to another entity, typically in exchange for goods, services, or to settle a debt.
-
C.
payerType
Indicates the type or category of entity responsible for making a payment in the relationship.
-
D.
reimbursementCategory
Indicates the classification or type under which a reimbursement claim or expense is categorized.
-
E.
paySystem
Indicates that one entity provides monetary or other compensation to another entity through a particular method, platform, or mechanism.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971e9576c81908eb59569af6da877 |
completed | April 10, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69d96fab4d0881909a7a4d66bab9aa85 |
completed | April 10, 2026, 9:46 p.m. |
| PDg | Predicate description generation | batch_69d970f6f5748190ad35aff801db53d5 |
completed | April 10, 2026, 9:51 p.m. |
Created at: April 9, 2026, 5:42 p.m.