Triple
T15198224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ticao Island |
E363193
|
entity |
| Predicate | hasDiveSite |
P41537
|
FINISHED |
| Object |
Manta Bowl
Manta Bowl is a renowned dive site off Ticao Island in the Philippines, famous for strong currents that attract manta rays and other pelagic marine life.
|
E1142870
|
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: Manta Bowl | Statement: [Ticao Island, hasDiveSite, Manta Bowl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manta Bowl Context triple: [Ticao Island, hasDiveSite, Manta Bowl]
-
A.
Manta Rota
Manta Rota is a coastal village and popular beach resort in Portugal’s Algarve region, known for its long sandy beach and calm, shallow waters.
-
B.
Manta
Manta is a municipality located in Almeidas Province in the Cundinamarca Department of central Colombia.
-
C.
Manta
Manta is a distributed object storage and compute service designed for running parallel computations directly on stored data in the cloud.
-
D.
Manta
Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
-
E.
Manta
Manta is a flying roller coaster at SeaWorld Orlando that simulates the graceful, gliding motion of a manta ray through a combination of high-speed thrills and aquatic theming.
- 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: Manta Bowl Triple: [Ticao Island, hasDiveSite, Manta Bowl]
Generated description
Manta Bowl is a renowned dive site off Ticao Island in the Philippines, famous for strong currents that attract manta rays and other pelagic marine life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Manta Bowl Target entity description: Manta Bowl is a renowned dive site off Ticao Island in the Philippines, famous for strong currents that attract manta rays and other pelagic marine life.
-
A.
Manta Rota
Manta Rota is a coastal village and popular beach resort in Portugal’s Algarve region, known for its long sandy beach and calm, shallow waters.
-
B.
Manta
Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
-
C.
Manta
Manta is a flying roller coaster at SeaWorld Orlando that simulates the graceful, gliding motion of a manta ray through a combination of high-speed thrills and aquatic theming.
-
D.
Manta
Manta is a distributed object storage and compute service designed for running parallel computations directly on stored data in the cloud.
-
E.
Manta
Manta is a municipality located in Almeidas Province in the Cundinamarca Department of central Colombia.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b476208190a5119710c518bb1f |
completed | April 15, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed3342624819087be35acadd88136 |
completed | May 9, 2026, 6:24 a.m. |
| NEDg | Description generation | batch_69fed516a2008190bab6da27d28289e7 |
completed | May 9, 2026, 6:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed57659a081909ec777549deff505 |
completed | May 9, 2026, 6:34 a.m. |
Created at: April 10, 2026, 3:10 a.m.