Triple
T17863772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FVR |
E446137
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object | Taguig, Metro Manila, Philippines |
—
|
NE NERFINISHED |
How this triple was built (3 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: Taguig, Metro Manila, Philippines | Statement: [FVR, placeOfDeath, Taguig, Metro Manila, Philippines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taguig, Metro Manila, Philippines Context triple: [FVR, placeOfDeath, Taguig, Metro Manila, Philippines]
-
A.
Muntinlupa, Philippines
Muntinlupa is a highly urbanized city in Metro Manila, Philippines, known for housing the New Bilibid Prison and serving as a major residential and commercial hub in the southern part of the capital region.
-
B.
Mandaluyong, Metro Manila, Philippines
Mandaluyong, in Metro Manila, Philippines, is a highly urbanized city known as a major commercial and business hub that hosts the headquarters of the Asian Development Bank.
-
C.
Binondo, Manila, Philippines
Binondo in Manila, Philippines is the city’s historic Chinatown district, renowned as one of the oldest Chinatowns in the world and a bustling center of commerce, culture, and cuisine.
-
D.
Pasay, Metro Manila, Philippines
Pasay, Metro Manila, Philippines is a highly urbanized coastal city in the National Capital Region known for hosting major transport hubs, commercial centers, and entertainment complexes.
-
E.
Santa Mesa, Manila
Santa Mesa, Manila is a primarily residential and commercial district in the eastern part of Manila, Philippines, known for its dense urban neighborhoods and proximity to major roads and educational institutions.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Taguig, Metro Manila, Philippines Target entity description: Taguig is a highly urbanized city in Metro Manila, Philippines, known for its rapid development and the Bonifacio Global City (BGC) financial and lifestyle district.
-
A.
Muntinlupa, Philippines
Muntinlupa is a highly urbanized city in Metro Manila, Philippines, known for housing the New Bilibid Prison and serving as a major residential and commercial hub in the southern part of the capital region.
-
B.
Mandaluyong, Metro Manila, Philippines
Mandaluyong, in Metro Manila, Philippines, is a highly urbanized city known as a major commercial and business hub that hosts the headquarters of the Asian Development Bank.
-
C.
Binondo, Manila, Philippines
Binondo in Manila, Philippines is the city’s historic Chinatown district, renowned as one of the oldest Chinatowns in the world and a bustling center of commerce, culture, and cuisine.
-
D.
Pasay, Metro Manila, Philippines
Pasay, Metro Manila, Philippines is a highly urbanized coastal city in the National Capital Region known for hosting major transport hubs, commercial centers, and entertainment complexes.
-
E.
Santa Mesa, Manila
Santa Mesa, Manila is a primarily residential and commercial district in the eastern part of Manila, Philippines, known for its dense urban neighborhoods and proximity to major roads and educational institutions.
- F. None of above. chosen
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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e49791b0c08190b04a426bd274065d |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 10:17 a.m.