Triple
T16815304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laoag Sinking Bell Tower |
E408728
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Laoag |
—
|
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: Laoag | Statement: [Laoag Sinking Bell Tower, locatedIn, Laoag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laoag Context triple: [Laoag Sinking Bell Tower, locatedIn, Laoag]
-
A.
Laoag
chosen
Laoag is a coastal city in northern Luzon, Philippines, known as the capital of Ilocos Norte and a regional center for commerce, education, and tourism.
-
B.
Tarlac City
Tarlac City is the capital and largest urban center of the province of Tarlac in the Central Luzon (Region III) area of the Philippines.
-
C.
Ilagan City
Ilagan City is a component city in the province of Isabela in the Philippines, known as an agricultural and commercial hub in the Cagayan Valley region.
-
D.
Tuguegarao City
Tuguegarao City is a major urban and commercial center in northeastern Luzon in the Philippines, known for its hot climate and role as a regional hub for education, trade, and government services.
-
E.
Meycauayan
Meycauayan is a highly urbanized city in the Philippine province of Bulacan known for its jewelry and leather industries.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b2e0e05081908bd5eaa64abe133d |
completed | April 18, 2026, 4:35 p.m. |
Created at: April 10, 2026, 5:23 a.m.