Triple
T8079552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NCR |
E188578
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Malabon |
E221038
|
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: Malabon | Statement: [NCR, contains, Malabon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malabon Context triple: [NCR, contains, Malabon]
-
A.
Malabon
chosen
Malabon is a coastal city in the northern part of Metro Manila in the Philippines, known for its historic districts, flood-prone waterways, and distinctive local cuisine.
-
B.
Mandaluyong
Mandaluyong is a highly urbanized city in the Philippines known as part of Metro Manila’s central business and commercial district.
-
C.
Taguig
Taguig is a highly urbanized city in Metro Manila in the Philippines, known for the Bonifacio Global City (BGC) business district and rapid commercial and residential development.
-
D.
Lungsod ng Pasig
Lungsod ng Pasig is a highly urbanized city in Metro Manila, Philippines, known as a major commercial and residential center that includes the Ortigas Center business district.
-
E.
Cubao
Cubao is a major commercial and transport hub in Quezon City, Metro Manila, known for its shopping centers, bus terminals, and entertainment venues.
- 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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb40a3f01c819096a2c9d5d5199fe6 |
completed | March 31, 2026, 3:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1c750c7c81908ad680f942bcbe9c |
completed | April 2, 2026, 7:36 a.m. |
Created at: March 30, 2026, 5:28 p.m.