Triple
T2375880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cebu |
E46197
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Oslob
Oslob is a coastal municipality in southern Cebu, Philippines, best known for its whale shark watching, beaches, and historic heritage sites.
|
E261529
|
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: Oslob | Statement: [Cebu, hasPart, Oslob]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oslob Context triple: [Cebu, hasPart, Oslob]
-
A.
Barajevo
Barajevo is a suburban municipality of Belgrade, Serbia, located in the southern part of the city’s administrative area.
-
B.
Borna
Borna is a town in the German state of Saxony that serves as an administrative and economic center in the Leipzig region.
-
C.
Gornji Grad
Gornji Grad is the historic upper town of Zagreb, known for its medieval streets, landmarks like St. Mark’s Church and the Stone Gate, and its role as the city’s political and cultural center.
-
D.
Bahía Negra
Bahía Negra is a remote riverside town in northern Paraguay, located in the Chaco region near the borders with Brazil and Bolivia.
-
E.
Lublinitz
Lublinitz is the former German name for the town of Lubliniec, located in southern Poland’s Silesian region.
- 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: Oslob Triple: [Cebu, hasPart, Oslob]
Generated description
Oslob is a coastal municipality in southern Cebu, Philippines, best known for its whale shark watching, beaches, and historic heritage sites.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oslob Target entity description: Oslob is a coastal municipality in southern Cebu, Philippines, best known for its whale shark watching, beaches, and historic heritage sites.
-
A.
Barajevo
Barajevo is a suburban municipality of Belgrade, Serbia, located in the southern part of the city’s administrative area.
-
B.
Borna
Borna is a town in the German state of Saxony that serves as an administrative and economic center in the Leipzig region.
-
C.
Gornji Grad
Gornji Grad is the historic upper town of Zagreb, known for its medieval streets, landmarks like St. Mark’s Church and the Stone Gate, and its role as the city’s political and cultural center.
-
D.
Bahía Negra
Bahía Negra is a remote riverside town in northern Paraguay, located in the Chaco region near the borders with Brazil and Bolivia.
-
E.
Lublinitz
Lublinitz is the former German name for the town of Lubliniec, located in southern Poland’s Silesian region.
- 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_69a88a1554a48190a0180682bcf099be |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abc794eee481908163148e1e666d9b |
completed | March 7, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea8ac3e80819099065f874f9dc25d |
completed | March 9, 2026, 11:02 a.m. |
| NEDg | Description generation | batch_69aeabd9a5a08190a2c6699576e36c46 |
completed | March 9, 2026, 11:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aead3299c88190af03577eef126387 |
completed | March 9, 2026, 11:21 a.m. |
Created at: March 4, 2026, 7:57 p.m.