Triple
T30864420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Shield Stamps showrooms |
E786156
|
entity |
| Predicate | hadAlternativeTo |
P170576
|
FINISHED |
| Object | cash purchase |
—
|
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: cash purchase | Statement: [Green Shield Stamps showrooms, hadAlternativeTo, cash purchase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadAlternativeTo Context triple: [Green Shield Stamps showrooms, hadAlternativeTo, cash purchase]
-
A.
hasAlternativeProgram
Indicates that one program is an alternative option or substitute for another program.
-
B.
hasAlternativeMedium
Indicates that an entity is available, expressed, or presented in another medium or format as an alternative to its primary one.
-
C.
hasAlternativeReconstruction
Indicates that there exists another possible way to reconstruct or represent the same underlying entity, structure, or configuration.
-
D.
hasAlternateTake
Indicates that one entity is an alternative version or different take of another, typically representing a variant recording, shot, or rendition of the same underlying content.
-
E.
hadCommon
Indicates that two or more entities shared the same attribute, experience, or element in common.
- 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_69f224b9df2c819086f55f8bcf7f382e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f691ab31288190afe04c1a55477a9f |
completed | May 3, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69f68b7d2794819092fef8a63f4f3de8 |
completed | May 2, 2026, 11:40 p.m. |
| PDg | Predicate description generation | batch_69f68fb914b88190b0cad83ea9fe9dfc |
completed | May 2, 2026, 11:58 p.m. |
Created at: April 29, 2026, 8:47 p.m.