Triple
T2520431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EDSA |
E55508
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Caloocan |
E214542
|
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: Caloocan | Statement: [EDSA, passesThrough, Caloocan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caloocan Context triple: [EDSA, passesThrough, Caloocan]
-
A.
Caloocan
chosen
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.
-
B.
Cavite City
Cavite City is a coastal urban center in the province of Cavite in the Philippines, historically significant as a former Spanish colonial port and naval base near Metro Manila.
-
C.
Muntinlupa
Muntinlupa is a highly urbanized city in the southern part of Metro Manila in the Philippines, known for housing the New Bilibid Prison and major commercial and residential developments like Alabang.
-
D.
Mandaluyong
Mandaluyong is a highly urbanized city in the Philippines known as part of Metro Manila’s central business and commercial district.
-
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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2343e3081908819dc58d8ff40ce |
completed | March 7, 2026, 7:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af6550c55481908fe4e8bab17aaa6d |
completed | March 10, 2026, 12:26 a.m. |
Created at: March 6, 2026, 9:46 p.m.