Triple
T19498293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cibao |
E487831
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Nagua
Nagua is a coastal city in the northeastern Dominican Republic, known as an important urban and commercial center in the Cibao region.
|
E1379264
|
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: Nagua | Statement: [Cibao, hasMajorCity, Nagua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagua Context triple: [Cibao, hasMajorCity, Nagua]
-
A.
Utila
Utila is a small Caribbean island off the coast of Honduras, known for its coral reefs, budget-friendly scuba diving, and diverse linguistic and cultural influences.
-
B.
Aguas Buenas
Aguas Buenas is a mountainous municipality in central Puerto Rico known for its cool climate, caves, and scenic rural landscapes.
-
C.
Mayaguana
Mayaguana is a sparsely populated, remote island in the southeastern Bahamas known for its unspoiled beaches, rich marine life, and laid-back, traditional Bahamian lifestyle.
-
D.
Bayamo
Bayamo is one of Cuba’s oldest colonial cities, historically significant as an early Spanish settlement and a center of Cuban independence sentiment.
-
E.
Abataranika
Abataranika is a Bengali literary work that served as the source material for the film "Mahanagar."
- 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: Nagua Triple: [Cibao, hasMajorCity, Nagua]
Generated description
Nagua is a coastal city in the northeastern Dominican Republic, known as an important urban and commercial center in the Cibao region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nagua Target entity description: Nagua is a coastal city in the northeastern Dominican Republic, known as an important urban and commercial center in the Cibao region.
-
A.
Utila
Utila is a small Caribbean island off the coast of Honduras, known for its coral reefs, budget-friendly scuba diving, and diverse linguistic and cultural influences.
-
B.
Aguas Buenas
Aguas Buenas is a mountainous municipality in central Puerto Rico known for its cool climate, caves, and scenic rural landscapes.
-
C.
Mayaguana
Mayaguana is a sparsely populated, remote island in the southeastern Bahamas known for its unspoiled beaches, rich marine life, and laid-back, traditional Bahamian lifestyle.
-
D.
Bayamo
Bayamo is one of Cuba’s oldest colonial cities, historically significant as an early Spanish settlement and a center of Cuban independence sentiment.
-
E.
Abataranika
Abataranika is a Bengali literary work that served as the source material for the film "Mahanagar."
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63493232881909fc33fb84da3e1b5 |
completed | April 20, 2026, 2:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0740622cd88190965b3eb6e644cb6f |
completed | May 15, 2026, 3:48 p.m. |
| NEDg | Description generation | batch_6a0744467d6c819082aa0933a0b9ba65 |
completed | May 15, 2026, 4:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0744ddbc3c8190b21d691a84e3ef92 |
completed | May 15, 2026, 4:07 p.m. |
Created at: April 10, 2026, 1:40 p.m.