Triple
T6445940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saipan International Airport |
E138341
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object |
Garapan
Garapan is the main commercial and tourist district of Saipan in the Northern Mariana Islands, known for its hotels, shops, and beachfront attractions.
|
E594785
|
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: Garapan | Statement: [Saipan International Airport, nearCity, Garapan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garapan Context triple: [Saipan International Airport, nearCity, Garapan]
-
A.
Surigaonon
Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
-
B.
Masbate
Masbate is an island province in the central Philippines, known for its cattle ranches, rodeo festivals, and location between Luzon and the Visayas.
-
C.
Apalit
Apalit is a municipality in the province of Pampanga in the Philippines, known for its religious festivals and riverside communities along the Pampanga River.
-
D.
Barrigada
Barrigada is a central village on the island of Guam that serves as a key residential and transportation hub.
-
E.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
- 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: Garapan Triple: [Saipan International Airport, nearCity, Garapan]
Generated description
Garapan is the main commercial and tourist district of Saipan in the Northern Mariana Islands, known for its hotels, shops, and beachfront attractions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Garapan Target entity description: Garapan is the main commercial and tourist district of Saipan in the Northern Mariana Islands, known for its hotels, shops, and beachfront attractions.
-
A.
Surigaonon
Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
-
B.
Masbate
Masbate is an island province in the central Philippines, known for its cattle ranches, rodeo festivals, and location between Luzon and the Visayas.
-
C.
Apalit
Apalit is a municipality in the province of Pampanga in the Philippines, known for its religious festivals and riverside communities along the Pampanga River.
-
D.
Barrigada
Barrigada is a central village on the island of Guam that serves as a key residential and transportation hub.
-
E.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
- 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_69c008aa61ac8190bc96715ed79fe2d8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0698d866c81909ef3e0a53833ff7d |
completed | March 22, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64bcd09e0819097eb60d13e8058dd |
completed | March 27, 2026, 9:20 a.m. |
| NEDg | Description generation | batch_69c64fba85a08190ad270b010294f86a |
completed | March 27, 2026, 9:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6508c2fb481909da94b4f67e95ecf |
completed | March 27, 2026, 9:40 a.m. |
Created at: March 22, 2026, 4:46 p.m.