Triple
T16563966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorsogon |
E402408
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Pilar |
E502813
|
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: Pilar | Statement: [Sorsogon, hasMunicipality, Pilar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pilar Context triple: [Sorsogon, hasMunicipality, Pilar]
-
A.
Pilar
Pilar is a Spanish royal, known formally as Infanta Pilar, Duchess of Badajoz, and a member of the House of Bourbon.
-
B.
Pilar
Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
-
C.
Pilar
chosen
Pilar is a coastal town on Siargao Island in the Philippines, known for its fishing communities and access to popular surfing and eco-tourism spots.
-
D.
Pilar
Pilar is a city in the Buenos Aires Province of Argentina, known as a growing residential and commercial hub within the Greater Buenos Aires metropolitan area.
-
E.
Pilar
Pilar is a municipality in the Philippine province of Abra, known for its rural highland landscapes and predominantly agricultural economy.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3577043048190bc9bcf55069b769f |
completed | April 18, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006edfe7f08190857fc6f66f3be9a0 |
completed | May 10, 2026, 11:41 a.m. |
Created at: April 10, 2026, 5:15 a.m.