Triple
T20986695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Managaha Island |
E516907
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Garapan |
—
|
NE NERFINISHED |
How this triple was built (2 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: [Managaha Island, nearbyCity, Garapan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garapan Context triple: [Managaha Island, nearbyCity, Garapan]
-
A.
Garapan
chosen
Garapan is the main commercial and tourist district of Saipan in the Northern Mariana Islands, known for its hotels, shops, and beachfront attractions.
-
B.
Balayan
Balayan is a historic coastal municipality in the province of Batangas in the Philippines, known for its heritage houses and annual Parada ng Lechon festival.
-
C.
Bansud
Bansud is a coastal agricultural municipality in the province of Oriental Mindoro in the Philippines.
-
D.
Surigaonon
Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
-
E.
Catanauan
Catanauan is a coastal municipality in the province of Quezon in the Philippines, known for its agricultural economy and fishing communities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ffac148190bbade9f0eceb660b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fbe31cec8190a1007414148b8abe |
completed | April 21, 2026, 4:24 a.m. |
Created at: April 16, 2026, 1:49 p.m.