Triple
T11712334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Employees’ Retirement System |
E278401
|
entity |
| Predicate | TSPIncludes |
P100912
|
FINISHED |
| Object | employee contributions |
—
|
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: employee contributions | Statement: [Federal Employees’ Retirement System, TSPIncludes, employee contributions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: TSPIncludes Context triple: [Federal Employees’ Retirement System, TSPIncludes, employee contributions]
-
A.
transportIncludes
Indicates that a broader transport operation or service encompasses, contains, or makes use of a specific transport segment, mode, or component as part of its overall movement.
-
B.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
-
C.
trailNetworkIncludes
Indicates that a specific trail network contains or encompasses a given trail segment or component as part of its overall system.
-
D.
travelMechanic
Indicates the method or system by which movement or travel between locations is carried out.
-
E.
travelRouteOf
Indicates the path or itinerary that an entity follows or uses when traveling from one location to another.
- 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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4be10088190854699385d1f6a95 |
completed | April 10, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69d88a7d483081909c2a101087515d74 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d890458d948190b15054c9ba0fd923 |
completed | April 10, 2026, 5:53 a.m. |
Created at: April 8, 2026, 9:40 p.m.