Triple
T12112478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PNR Metro Commuter Line |
E288466
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object |
Santa Rosa
Santa Rosa is a city in the province of Laguna in the Philippines, known as a rapidly urbanizing industrial and residential hub south of Metro Manila.
|
E445213
|
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 Rosa | Statement: [PNR Metro Commuter Line, terminus, Santa Rosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Rosa Context triple: [PNR Metro Commuter Line, terminus, Santa Rosa]
-
A.
Santa Rosa
Santa Rosa is a mid-sized city in Sonoma County known as a cultural and economic hub of California’s wine country.
-
B.
Santa Rosa
Santa Rosa is a residential barrio (neighborhood) within the municipality of Dorado, Puerto Rico.
-
C.
Santa Rosa
Santa Rosa is the principal city and administrative center of Argentina’s La Pampa Province, known for its role as a regional hub in the country’s central plains.
-
D.
Santa Rosa
Santa Rosa is a small settlement located on Santa Cruz Island in the Galápagos archipelago of Ecuador.
-
E.
Santa Rosa
Santa Rosa is a coastal city in southwestern Ecuador known for its agriculture, shrimp farming, and role as a commercial center in El Oro Province.
- 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 Rosa Triple: [PNR Metro Commuter Line, terminus, Santa Rosa]
Generated description
Santa Rosa is a city in the province of Laguna in the Philippines, known as a rapidly urbanizing industrial and residential hub south of Metro Manila.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Santa Rosa Target entity description: Santa Rosa is a city in the province of Laguna in the Philippines, known as a rapidly urbanizing industrial and residential hub south of Metro Manila.
-
A.
Santa Rosa
chosen
Santa Rosa is a rapidly urbanizing city in the Philippine province of Laguna, known as a major industrial, commercial, and residential hub in the Calabarzon region.
-
B.
Santa Rosa
Santa Rosa is a coastal city in southwestern Ecuador known for its agriculture, shrimp farming, and role as a commercial center in El Oro Province.
-
C.
Santa Rosa
Santa Rosa is the principal city and administrative center of Argentina’s La Pampa Province, known for its role as a regional hub in the country’s central plains.
-
D.
Santa Rosa
Santa Rosa is a residential barrio (neighborhood) within the municipality of Dorado, Puerto Rico.
-
E.
Santa Rosa
Santa Rosa is a small settlement located on Santa Cruz Island in the Galápagos archipelago of Ecuador.
- F. None of above.
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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9156814148190b47d63a89fcab17c |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a7fd708819090af422a60a69859 |
completed | May 2, 2026, 4:46 p.m. |
| NEDg | Description generation | batch_69f62c54e6b08190bdae0ec35cc1c48d |
completed | May 2, 2026, 4:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f62d0c2568819083e66c8ae484d30d |
completed | May 2, 2026, 4:57 p.m. |
Created at: April 8, 2026, 9:49 p.m.