Triple
T7422463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Target Corporation |
E171282
|
entity |
| Predicate | brandName |
P1500
|
FINISHED |
| Object |
Good & Gather
Good & Gather is Target's flagship private-label food and beverage brand offering a wide range of everyday grocery products.
|
E664361
|
NE FINISHED |
How this triple was built (4 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: Good & Gather | Statement: [Target Corporation, brandName, Good & Gather]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Good & Gather Context triple: [Target Corporation, brandName, Good & Gather]
-
A.
Good's Food to Go
Good's Food to Go is a casual quick-service dining location at Disney’s Old Key West Resort offering convenient grab-and-go meals and snacks for guests.
-
B.
Shipmeadow
Shipmeadow is a small rural village and civil parish located in the county of Suffolk in eastern England.
-
C.
The Cheese Board Collective
The Cheese Board Collective is a renowned worker-owned bakery, pizzeria, and cheese shop in Berkeley, California, celebrated for its artisanal offerings and cooperative business model.
-
D.
Rosie’s
Rosie’s is the women’s jail facility on New York City’s Rikers Island, officially known as the Rose M. Singer Center.
-
E.
Cookhouse
Cookhouse is a small rural town in South Africa’s Eastern Cape, known historically as a farming and railway junction settlement.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Good & Gather Triple: [Target Corporation, brandName, Good & Gather]
Generated description
Good & Gather is Target's flagship private-label food and beverage brand offering a wide range of everyday grocery products.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Good & Gather Target entity description: Good & Gather is Target's flagship private-label food and beverage brand offering a wide range of everyday grocery products.
-
A.
Good's Food to Go
Good's Food to Go is a casual quick-service dining location at Disney’s Old Key West Resort offering convenient grab-and-go meals and snacks for guests.
-
B.
Shipmeadow
Shipmeadow is a small rural village and civil parish located in the county of Suffolk in eastern England.
-
C.
The Cheese Board Collective
The Cheese Board Collective is a renowned worker-owned bakery, pizzeria, and cheese shop in Berkeley, California, celebrated for its artisanal offerings and cooperative business model.
-
D.
Rosie’s
Rosie’s is the women’s jail facility on New York City’s Rikers Island, officially known as the Rose M. Singer Center.
-
E.
Cookhouse
Cookhouse is a small rural town in South Africa’s Eastern Cape, known historically as a farming and railway junction settlement.
- F. None of above. chosen
Provenance (5 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_69c68a625d048190af70eb8b63bec5a0 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2ed29ec8190804564185fe20797 |
completed | March 27, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c81effc488819086336eea92604fa8 |
completed | March 28, 2026, 6:33 p.m. |
| NEDg | Description generation | batch_69c81fe025d081909f2a5c4515c60f64 |
completed | March 28, 2026, 6:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c824010104819081977e89d79ebb44 |
completed | March 28, 2026, 6:54 p.m. |
Created at: March 27, 2026, 3:11 p.m.