Triple
T2677667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Araneta City |
E56497
|
entity |
| Predicate | district |
P2709
|
FINISHED |
| Object | Cubao |
E56239
|
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: Cubao | Statement: [Araneta City, district, Cubao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cubao Context triple: [Araneta City, district, Cubao]
-
A.
Cubao
chosen
Cubao is a major commercial and transport hub in Quezon City, Metro Manila, known for its shopping centers, bus terminals, and entertainment venues.
-
B.
Malabon
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.
-
C.
Loyola Heights, Quezon City
Loyola Heights, Quezon City is an upscale residential and educational district in Quezon City, Metro Manila, known for its universities, exclusive subdivisions, and vibrant commercial areas.
-
D.
Mandaluyong
Mandaluyong is a highly urbanized city in the Philippines known as part of Metro Manila’s central business and commercial district.
-
E.
Caloocan
Caloocan is a highly urbanized city in the Philippines that forms part of the northern section of Metro Manila and serves as a major residential and commercial hub.
- 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_69ab4a4b13fc81909dfdb3f23da46832 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd9b697fc8190a5ec8b75ee2ad238 |
completed | March 7, 2026, 7:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa065a6f48190973a3b6c52aa23bf |
completed | March 10, 2026, 4:39 a.m. |
Created at: March 6, 2026, 9:54 p.m.