Triple
T21266322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GIFT City |
E524136
|
entity |
| Predicate | offersIncentiveType |
P7916
|
FINISHED |
| Object | tax incentives for financial services |
—
|
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: tax incentives for financial services | Statement: [GIFT City, offersIncentiveType, tax incentives for financial services]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersIncentiveType Context triple: [GIFT City, offersIncentiveType, tax incentives for financial services]
-
A.
offersIncentive
Indicates that one entity provides a reward, benefit, or motivation to another entity to encourage a specific action or behavior.
-
B.
typeOfIncentive
chosen
Indicates the specific kind or category of incentive associated with an entity or action.
-
C.
offersProgram
Indicates that an entity provides or makes available a specific program (such as a course, curriculum, or initiative).
-
D.
offersPass
Indicates that one entity provides or makes available a pass (such as a ticket, permit, or access credential) to another entity.
-
E.
offersRewardFor
Indicates that one entity promises or provides a reward in exchange for another entity performing a specified action or achieving a particular outcome.
- 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_69e0b5156d7881909bd4f83676590715 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e735eca49081908e4f13fcab717c41 |
completed | April 21, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69e5f6161dac8190b06009cd180e3ff7 |
completed | April 20, 2026, 9:47 a.m. |
Created at: April 16, 2026, 4 p.m.