Triple
T2632083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mandaluyong |
E59656
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Pasay |
E188579
|
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: Pasay | Statement: [Mandaluyong, borderedBy, Pasay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pasay Context triple: [Mandaluyong, borderedBy, Pasay]
-
A.
Pasay
chosen
Pasay is a highly urbanized coastal city in the Philippines known for its entertainment complexes, shopping centers, and proximity to Manila’s main international airport.
-
B.
Malpaso
Malpaso is the highest peak on the Canary Island of El Hierro, known for its panoramic views over the island and surrounding Atlantic Ocean.
-
C.
Tanjay
Tanjay is a component city in the province of Negros Oriental in the Philippines, known for its agricultural economy and cultural festivals.
-
D.
Los Baños
Los Baños is a municipality in the Philippines known as a major center for agricultural research and education, particularly in rice science.
-
E.
Las Piñas
Las Piñas is a highly urbanized city in the southern part of Metro Manila in the Philippines, known for its residential communities and the historic Bamboo Organ.
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8c6e540819087c7f92432b27b0f |
completed | March 7, 2026, 7:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af90a996188190b3a83a31e69d09ef |
completed | March 10, 2026, 3:31 a.m. |
Created at: March 6, 2026, 9:50 p.m.