Triple
T4476556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippine Scouts |
E100021
|
entity |
| Predicate | payScale |
P56782
|
FINISHED |
| Object | U.S. Army pay rates for enlisted personnel |
—
|
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: U.S. Army pay rates for enlisted personnel | Statement: [Philippine Scouts, payScale, U.S. Army pay rates for enlisted personnel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: payScale Context triple: [Philippine Scouts, payScale, U.S. Army pay rates for enlisted personnel]
-
A.
payGrade
Indicates the level or category of compensation assigned to an entity, typically reflecting its rank, role, or seniority in a pay structure.
-
B.
careerEarningsApprox
Indicates an approximate total amount of money an entity has earned over the course of its career.
-
C.
salary
Indicates the amount of monetary compensation an entity receives, typically on a regular basis, for work or services performed.
-
D.
payGradeComparison
Indicates that the relative pay grade or salary level between two entities is being compared (e.g., one is higher, lower, or equal to the other).
-
E.
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.
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35728ed508190ba0e882fa62d8848 |
completed | March 13, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69b3563d63008190816e37027e761375 |
completed | March 13, 2026, 12:11 a.m. |
| PDg | Predicate description generation | batch_69b35727a8ac819090420bd3e2cfbcf1 |
completed | March 13, 2026, 12:15 a.m. |
Created at: March 12, 2026, 11:35 p.m.