Triple
T20033735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pennsylvania proprietors |
E497203
|
entity |
| Predicate | compensationYear |
P138449
|
FINISHED |
| Object | late 18th century |
—
|
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: late 18th century | Statement: [Pennsylvania proprietors, compensationYear, late 18th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: compensationYear Context triple: [Pennsylvania proprietors, compensationYear, late 18th century]
-
A.
compensationModel
Indicates the type or structure of payment or rewards provided in exchange for work, services, or performance.
-
B.
compensationReason
Indicates the reason or cause for which compensation is granted, requested, or applied between entities.
-
C.
compensationCategory
Indicates the type or classification of compensation associated with an entity, such as how or in what form payment or remuneration is provided.
-
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.
compensationProgram
Indicates a relationship where an entity provides or participates in a structured plan that offers payment or benefits in return for work, services, or losses.
- 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_69da627278c88190babe4297a9df1236 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e662e6a7e481908069c1de2b3f94e0 |
completed | April 20, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69e54ce752748190a0a1ffddd0372271 |
completed | April 19, 2026, 9:45 p.m. |
| PDg | Predicate description generation | batch_69e54fc20888819083c9118a09d0d2dc |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 11, 2026, 3:36 p.m.