Triple
T13532369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tampa Tarpons |
E323164
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
Tarpons
The Tarpons are a Minor League Baseball team based in Tampa, Florida, serving as a lower-level affiliate in the New York Yankees organization.
|
E1047092
|
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: Tarpons | Statement: [Tampa Tarpons, shortName, Tarpons]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarpons Context triple: [Tampa Tarpons, shortName, Tarpons]
-
A.
Tiburon
Tiburon is a small, affluent waterfront town in Marin County, California, known for its scenic views of San Francisco Bay and ferry access to nearby islands.
-
B.
Tiburon
Tiburon is a coastal commune in southwestern Haiti known for its location on the Tiburon Peninsula along the Caribbean Sea.
-
C.
Tayassu
Tayassu is a genus of New World peccaries, medium-sized pig-like mammals native to Central and South American forests and scrublands.
-
D.
Marlin
Marlin is the cautious and devoted clownfish father from Pixar's "Finding Nemo" franchise, known for his ocean-spanning quest to rescue his son.
-
E.
Ladyfish
Ladyfish is a supporting animated fish character who serves as the love interest of the title character in the 1964 fantasy-comedy film "The Incredible Mr. Limpet."
- 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: Tarpons Triple: [Tampa Tarpons, shortName, Tarpons]
Generated description
The Tarpons are a Minor League Baseball team based in Tampa, Florida, serving as a lower-level affiliate in the New York Yankees organization.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tarpons Target entity description: The Tarpons are a Minor League Baseball team based in Tampa, Florida, serving as a lower-level affiliate in the New York Yankees organization.
-
A.
Tiburon
Tiburon is a small, affluent waterfront town in Marin County, California, known for its scenic views of San Francisco Bay and ferry access to nearby islands.
-
B.
Tiburon
Tiburon is a coastal commune in southwestern Haiti known for its location on the Tiburon Peninsula along the Caribbean Sea.
-
C.
Tayassu
Tayassu is a genus of New World peccaries, medium-sized pig-like mammals native to Central and South American forests and scrublands.
-
D.
Marlin
Marlin is the cautious and devoted clownfish father from Pixar's "Finding Nemo" franchise, known for his ocean-spanning quest to rescue his son.
-
E.
Ladyfish
Ladyfish is a supporting animated fish character who serves as the love interest of the title character in the 1964 fantasy-comedy film "The Incredible Mr. Limpet."
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafbb34548190a6b44faa48125cd4 |
completed | April 12, 2026, 2:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d95ce008190a915fe5865c38ee5 |
completed | May 3, 2026, 2:37 p.m. |
| NEDg | Description generation | batch_69f75f9c6a8881908a6df0a9a4bbc7ab |
completed | May 3, 2026, 2:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7601770ac8190a03be23cfe66d5d1 |
completed | May 3, 2026, 2:47 p.m. |
Created at: April 9, 2026, 9:44 p.m.