Triple
T1025745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biscayne Bay |
E22135
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Card Sound
Card Sound is a shallow, mangrove-fringed body of water in southern Florida that forms part of the northern Florida Keys and connects Biscayne Bay to Florida Bay.
|
E119889
|
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: Card Sound | Statement: [Biscayne Bay, hasPart, Card Sound]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Card Sound Context triple: [Biscayne Bay, hasPart, Card Sound]
-
A.
Card
Card is a surname most notably borne by Andrew Card, a former White House Chief of Staff under U.S. President George W. Bush.
-
B.
Cardiac Cards
Cardiac Cards is a nickname for the former St. Louis Cardinals NFL team, referencing their dramatic, late-game comebacks and close finishes.
-
C.
CharlieCard
The CharlieCard is a reusable contactless smart card used to pay fares on Boston's MBTA public transit system.
-
D.
CharmCard
CharmCard is a contactless smart fare card used for paying transit fares across the Maryland Transit Administration’s bus, rail, and other public transportation services.
-
E.
Calling Cards
Calling Cards is a program section of the Telluride Film Festival that showcases emerging filmmakers’ early or breakthrough works.
- 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: Card Sound Triple: [Biscayne Bay, hasPart, Card Sound]
Generated description
Card Sound is a shallow, mangrove-fringed body of water in southern Florida that forms part of the northern Florida Keys and connects Biscayne Bay to Florida Bay.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Card Sound Target entity description: Card Sound is a shallow, mangrove-fringed body of water in southern Florida that forms part of the northern Florida Keys and connects Biscayne Bay to Florida Bay.
-
A.
Card
Card is a surname most notably borne by Andrew Card, a former White House Chief of Staff under U.S. President George W. Bush.
-
B.
Cardiac Cards
Cardiac Cards is a nickname for the former St. Louis Cardinals NFL team, referencing their dramatic, late-game comebacks and close finishes.
-
C.
CharlieCard
The CharlieCard is a reusable contactless smart card used to pay fares on Boston's MBTA public transit system.
-
D.
CharmCard
CharmCard is a contactless smart fare card used for paying transit fares across the Maryland Transit Administration’s bus, rail, and other public transportation services.
-
E.
Calling Cards
Calling Cards is a program section of the Telluride Film Festival that showcases emerging filmmakers’ early or breakthrough works.
- 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_69a493d6e380819097b384986ffc315c |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7f4c66c8190b6098fb72c1465a3 |
completed | March 1, 2026, 10:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bba40f88190b80010a837dfb1cc |
completed | March 7, 2026, 2:52 p.m. |
| NEDg | Description generation | batch_69ac3c7d16748190a95aaffd04a867b3 |
completed | March 7, 2026, 2:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac3ce827b88190a5de06c695ad4ecb |
completed | March 7, 2026, 2:57 p.m. |
Created at: March 1, 2026, 7:41 p.m.