Triple
T16107672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | República Filipina |
E390781
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Malolos |
E385995
|
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: Malolos | Statement: [República Filipina, capital, Malolos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malolos Context triple: [República Filipina, capital, Malolos]
-
A.
Malolos
chosen
Malolos is a historic city in the Philippines best known as the birthplace of the First Philippine Republic and the site of the Malolos Congress.
-
B.
Paete
Paete is a lakeside municipality in the Philippine province of Laguna renowned for its skilled woodcarving and papier-mâché artisans.
-
C.
Sta. Cruz
Sta. Cruz is a coastal municipality in the province of Ilocos Sur in the Philippines, known for its agricultural economy and proximity to the South China Sea.
-
D.
Sta. Cruz
Sta. Cruz is a coastal municipality in the province of Zambales in the Philippines, known for its fishing communities and proximity to the West Philippine Sea.
-
E.
Dasmariñas
Dasmariñas is a rapidly urbanizing city in the province of Cavite in the Philippines, known as a major residential, commercial, and educational hub south of Metro Manila.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff6e55c08190b77f344e4e8c42ad |
completed | April 17, 2026, 9:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeba4479c81909f7d43e33f228f7e |
completed | May 10, 2026, 2:21 a.m. |
Created at: April 10, 2026, 5 a.m.