Triple
T28326534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tax-Deferred Annuity Program |
E717423
|
entity |
| Predicate | benefitPhase |
P158318
|
FINISHED |
| Object | provides income in retirement |
—
|
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: provides income in retirement | Statement: [Tax-Deferred Annuity Program, benefitPhase, provides income in retirement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitPhase Context triple: [Tax-Deferred Annuity Program, benefitPhase, provides income in retirement]
-
A.
benefice
Indicates that one entity grants or bestows a benefit, favor, or advantage upon another.
-
B.
benefitMode
chosen
Indicates the manner or method through which a benefit is provided, received, or realized in the relationship.
-
C.
benefitCharacteristic
Indicates that one entity possesses a quality or feature that provides an advantage, usefulness, or positive effect to another entity.
-
D.
benefitsState
Indicates that one entity provides an advantage, improvement, or positive outcome to a state or governmental entity.
-
E.
beneficeType
Indicates the specific category or kind of benefice (ecclesiastical office or endowed church position) associated with an entity.
- 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_69eff6e9a57c8190a69c2c74b5d72119 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f69383222c81909d8baa04129d5c81 |
completed | May 3, 2026, 12:14 a.m. |
| PD | Predicate disambiguation | batch_69f690eb1e948190aab41a89969519a5 |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 28, 2026, 12:28 a.m.