Triple
T19277652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mavericks (Colorado Mesa University) |
E482099
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object |
Maverick
Maverick is the official mascot character representing Colorado Mesa University’s athletic teams.
|
E1367673
|
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: [Mavericks (Colorado Mesa University), mascot, Maverick]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maverick Context triple: [Mavericks (Colorado Mesa University), mascot, Maverick]
-
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 high-speed steel roller coaster at Cedar Point in Ohio, renowned for its intense launches, inversions, and twisted track layout.
-
C.
Maverick
Maverick is a cigarette brand known for its budget-friendly positioning within the U.S. tobacco market.
-
D.
Maverick
Maverick is the codename used by Chris Bradley, a minor Marvel Comics character associated with the X-Men universe who possesses mutant electrical powers.
-
E.
Maverick
Maverick is an entertainment company co-founded by Madonna that has operated in music, film, and artist management.
- 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: [Mavericks (Colorado Mesa University), mascot, Maverick]
Generated description
Maverick is the official mascot character representing Colorado Mesa University’s athletic teams.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maverick Target entity description: Maverick is the official mascot character representing Colorado Mesa University’s athletic teams.
-
A.
Maverick
Maverick is the codename used by Chris Bradley, a minor Marvel Comics character associated with the X-Men universe who possesses mutant electrical powers.
-
B.
Maverick
Maverick is a political nickname for U.S. Senator John McCain, reflecting his reputation for independence and willingness to break with his party.
-
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 an MBTA subway station on Boston’s Blue Line serving the East Boston neighborhood.
-
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
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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fbbe6e9c8190a7fa2ef3aa598e6c |
completed | April 20, 2026, 10:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a070e7929a081908362870dcfaeec4c |
completed | May 15, 2026, 12:15 p.m. |
| NEDg | Description generation | batch_6a070ef513888190b8c9f8c2de36f234 |
completed | May 15, 2026, 12:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a070fe766d081909453b1f8563817e8 |
completed | May 15, 2026, 12:21 p.m. |
Created at: April 10, 2026, 1:30 p.m.