Triple
T8079539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NCR |
E188578
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | City of 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: City of Manila | Statement: [NCR, contains, City of Manila]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of Manila Context triple: [NCR, contains, City of Manila]
-
A.
San Miguel, Manila
San Miguel, Manila is a historic district in the city of Manila, Philippines, known for housing the presidential Malacañang Palace and various government and educational institutions.
-
B.
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.
-
C.
Manila
Manila is the OpenStack shared file system service that provides scalable, API-driven management of networked file shares.
-
D.
Metro Manila
Metro Manila is the densely populated national capital region of the Philippines, encompassing Manila and several surrounding cities as the country’s political, economic, and cultural center.
-
E.
Quezon City
Quezon City is a major urban center in Metro Manila known for hosting many national government institutions, universities, and media networks in the Philippines.
- 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_69cceceb7fa48190b1013a25fd8f14a5 |
completed | April 1, 2026, 10:01 a.m. |
Created at: March 30, 2026, 5:28 p.m.