Triple
T361083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TrueBlue |
E7852
|
entity |
| Predicate | benefitCategory |
P10121
|
FINISHED |
| Object | travel rewards |
—
|
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: travel rewards | Statement: [TrueBlue, benefitCategory, travel rewards]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitCategory Context triple: [TrueBlue, benefitCategory, travel rewards]
-
A.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
B.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
C.
benefitForm
chosen
Indicates that one entity is a specific form, type, or variant in which a benefit is provided or realized for another entity.
-
D.
beneficiaries
Indicates that certain entities receive advantages, profits, or positive outcomes from an action, event, or arrangement.
-
E.
benefitedCountry
Indicates that one country gains an advantage, profit, or positive outcome from an action, event, or entity associated with another.
- 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_69a2e7e880008190a6ad7e06e5d03007 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebce64c88190a0a8edcc7095f78b |
completed | Feb. 28, 2026, 1:21 p.m. |
| PD | Predicate disambiguation | batch_69a2e95c843c8190b2aba9af6e869ba1 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.