Triple
T7750887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LO |
E175756
|
entity |
| Predicate | associatedBrand |
P1500
|
FINISHED |
| Object |
Maverick
Maverick is a cigarette brand known for its budget-friendly positioning within the U.S. tobacco market.
|
E685594
|
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: Maverick | Statement: [LO, associatedBrand, Maverick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maverick Context triple: [LO, associatedBrand, Maverick]
-
A.
Maverick
Maverick is an MBTA subway station on Boston’s Blue Line serving the East Boston neighborhood.
-
B.
Maverick
Maverick is a 1994 comedic Western film starring Mel Gibson, Jodie Foster, and James Garner, centered on a charming gambler trying to raise money for a high-stakes poker tournament.
-
C.
Maverick
Maverick is a classic American Western comedy television series that aired in the late 1950s, following the adventures of charming, poker-playing gambler Bret Maverick and his relatives.
-
D.
Maverick
Maverick is a political nickname for U.S. Senator John McCain, reflecting his reputation for independence and willingness to break with his party.
-
E.
Maverick
Maverick is a high-speed steel roller coaster at Cedar Point in Ohio, renowned for its intense launches, inversions, and twisted track layout.
- 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: Maverick Triple: [LO, associatedBrand, Maverick]
Generated description
Maverick is a cigarette brand known for its budget-friendly positioning within the U.S. tobacco market.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maverick Target entity description: Maverick is a cigarette brand known for its budget-friendly positioning within the U.S. tobacco market.
-
A.
Maverick
Maverick is a political nickname for U.S. Senator John McCain, reflecting his reputation for independence and willingness to break with his party.
-
B.
Maverick
Maverick is a classic American Western comedy television series that aired in the late 1950s, following the adventures of charming, poker-playing gambler Bret Maverick and his relatives.
-
C.
Maverick
Maverick is the daring U.S. Navy fighter pilot Pete "Maverick" Mitchell, the iconic lead character of the Top Gun film series.
-
D.
Maverick
Maverick is a high-speed steel roller coaster at Cedar Point in Ohio, renowned for its intense launches, inversions, and twisted track layout.
-
E.
Maverick
Maverick is a 1994 comedic Western film starring Mel Gibson, Jodie Foster, and James Garner, centered on a charming gambler trying to raise money for a high-stakes poker tournament.
- 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_69c69960b3588190a53aa590d31d9544 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c703b264c0819095c37534a676531d |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be576274819092e5ebdbcf2361da |
completed | March 29, 2026, 5:53 a.m. |
| NEDg | Description generation | batch_69c8bf141c8081908c33dab4d90943b0 |
completed | March 29, 2026, 5:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8bf8c4bdc81909c74f7b4ec524177 |
completed | March 29, 2026, 5:58 a.m. |
Created at: March 27, 2026, 4:08 p.m.