Triple
T10685548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silliman University |
E251867
|
entity |
| Predicate | hasDemonym |
P191
|
FINISHED |
| Object |
Sillimanian
A Sillimanian is a person affiliated with Silliman University, typically as a student, alumnus, or member of its academic community.
|
E879022
|
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: Sillimanian | Statement: [Silliman University, hasDemonym, Sillimanian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sillimanian Context triple: [Silliman University, hasDemonym, Sillimanian]
-
A.
Karagawan
Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
-
B.
Danao
Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
-
C.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
D.
Samar
Samar is a surname most notably associated with Sima Samar, an Afghan human rights advocate, physician, and former minister.
-
E.
Samar
Samar is a critically acclaimed Indian film directed by Shyam Benegal that explores themes of caste, power, and social injustice in rural India.
- 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: Sillimanian Triple: [Silliman University, hasDemonym, Sillimanian]
Generated description
A Sillimanian is a person affiliated with Silliman University, typically as a student, alumnus, or member of its academic community.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sillimanian Target entity description: A Sillimanian is a person affiliated with Silliman University, typically as a student, alumnus, or member of its academic community.
-
A.
Karagawan
Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
-
B.
Danao
Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
-
C.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
D.
Samar
Samar is a surname most notably associated with Sima Samar, an Afghan human rights advocate, physician, and former minister.
-
E.
Samar
Samar is a critically acclaimed Indian film directed by Shyam Benegal that explores themes of caste, power, and social injustice in rural India.
- 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd182d7c819099ff6ffb3a7083f5 |
completed | April 9, 2026, 1:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d98894cea48190877a015dcb645bee |
completed | April 10, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69d98aeb82988190a17b009c74279423 |
completed | April 10, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d98c2aae048190b348e5614ff23f03 |
completed | April 10, 2026, 11:47 p.m. |
Created at: April 8, 2026, 9:10 p.m.