Triple
T18002051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taytay |
E430651
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Cainta |
—
|
NE NERFINISHED |
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: Cainta | Statement: [Taytay, borderedBy, Cainta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cainta Context triple: [Taytay, borderedBy, Cainta]
-
A.
Cainta
chosen
Cainta is a highly urbanized municipality in the province of Rizal, Philippines, known as one of the country’s most populous and economically active suburban areas adjacent to Metro Manila.
-
B.
Montalban
Montalban is a surname of Spanish origin borne by various notable individuals in the arts and entertainment.
-
C.
Antipolo
Antipolo is a rural barangay in the municipality of San Antonio in the province of Zambales, Philippines.
-
D.
Antipolo
Antipolo is a city in the province of Rizal, Philippines, known as a pilgrimage site and suburban residential area east of Metro Manila.
-
E.
Tayabas
Tayabas is a historic city in the province of Quezon in the Calabarzon region of the Philippines, known for its Spanish-era heritage structures and cultural festivals.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b3e9498c8190bdfa7a53b0c0d8db |
completed | April 19, 2026, 10:52 a.m. |
Created at: April 10, 2026, 10:23 a.m.