Triple
T101631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippines |
E2051
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Manila |
E7896
|
NE FINISHED |
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: Manila | Statement: [Philippines, capital, Manila]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manila Context triple: [Philippines, capital, Manila]
-
A.
Manila
chosen
Manila is the capital city of the Philippines, a historic and densely populated coastal metropolis that has long served as the country’s political, economic, and cultural center.
-
B.
Philippines
The Philippines is a Southeast Asian archipelagic country in the western Pacific Ocean known for its diverse culture, colonial history, and thousands of islands.
-
C.
Saigon
Saigon, now officially known as Ho Chi Minh City, is Vietnam’s largest city and a historic economic and cultural hub in the south of the country.
-
D.
Batavia
Batavia was the principal colonial capital of the Dutch East Indies, located on the site of present-day Jakarta in Indonesia.
-
E.
Tokyo
Tokyo is Japan’s largest metropolis and a global center of finance, culture, technology, and transportation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a256a8b6d0819083838a9708759407 |
completed | Feb. 28, 2026, 2:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a266ee56548190a781e2d0ea7fac2b |
completed | Feb. 28, 2026, 3:54 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.