Triple
T16979138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Ignacia |
E411894
|
entity |
| Predicate | hasBarangay |
P29835
|
FINISHED |
| Object |
Santa Juliana
Santa Juliana is a rural barangay of the municipality of Santa Ignacia in Tarlac province, Philippines.
|
E1246311
|
NE FINISHED |
How this triple was built (4 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: Santa Juliana | Statement: [Santa Ignacia, hasBarangay, Santa Juliana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Juliana Context triple: [Santa Ignacia, hasBarangay, Santa Juliana]
-
A.
Santa Ines
Santa Ines is a barangay (village-level administrative division) within the municipality of Santa Ignacia in the Philippines.
-
B.
Santa Tereza
Santa Tereza is a small wine-producing town in Brazil’s Serra Gaúcha region, known for its Italian heritage and scenic mountain landscapes.
-
C.
Santa Ifigênia
Santa Ifigênia is a historic central neighborhood in São Paulo, Brazil, known for its bustling electronics commerce and proximity to major downtown landmarks.
-
D.
Santa Marcela
Santa Marcela is a rural municipality in the province of Apayao in the Cordillera Administrative Region of the Philippines.
-
E.
Santa Rosalía
Santa Rosalía is a historic mining town and port on the eastern coast of the Baja California Peninsula in Mexico, known for its French-influenced architecture and copper mining heritage.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Santa Juliana Triple: [Santa Ignacia, hasBarangay, Santa Juliana]
Generated description
Santa Juliana is a rural barangay of the municipality of Santa Ignacia in Tarlac province, Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Santa Juliana Target entity description: Santa Juliana is a rural barangay of the municipality of Santa Ignacia in Tarlac province, Philippines.
-
A.
Santa Ines
Santa Ines is a barangay (village-level administrative division) within the municipality of Santa Ignacia in the Philippines.
-
B.
Santa Tereza
Santa Tereza is a small wine-producing town in Brazil’s Serra Gaúcha region, known for its Italian heritage and scenic mountain landscapes.
-
C.
Santa Ifigênia
Santa Ifigênia is a historic central neighborhood in São Paulo, Brazil, known for its bustling electronics commerce and proximity to major downtown landmarks.
-
D.
Santa Marcela
Santa Marcela is a rural municipality in the province of Apayao in the Cordillera Administrative Region of the Philippines.
-
E.
Santa Rosalía
Santa Rosalía is a historic mining town and port on the eastern coast of the Baja California Peninsula in Mexico, known for its French-influenced architecture and copper mining heritage.
- F. None of above. chosen
Provenance (5 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d185a9408190a991bf8a1ef694f0 |
completed | April 18, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b412dd48190862fde6d1656113d |
completed | May 10, 2026, 11:56 p.m. |
| NEDg | Description generation | batch_6a011d109dbc819086b563e55babc714 |
completed | May 11, 2026, 12:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a011d7568ec8190a2278dfa3ba29a63 |
completed | May 11, 2026, 12:06 a.m. |
Created at: April 10, 2026, 5:32 a.m.