Triple
T8161843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laguna |
E190593
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Lumban
Lumban is a municipality in the Philippine province of Laguna known for its traditional hand-embroidered textiles and scenic lakeside setting along Laguna de Bay.
|
E719874
|
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: Lumban | Statement: [Laguna, hasMunicipality, Lumban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lumban Context triple: [Laguna, hasMunicipality, Lumban]
-
A.
Bolango-Bulango
Bolango-Bulango is an Austronesian language spoken by the Bolango people in northern Sulawesi, Indonesia.
-
B.
Bulukumba
Bulukumba is a regency in South Sulawesi, Indonesia, known for its coastal landscapes, traditional boatbuilding, and Makassarese cultural heritage.
-
C.
Belawa
Belawa is a town and administrative district located within Wajo Regency in South Sulawesi, Indonesia.
-
D.
Batusangkar
Batusangkar is a historic town in West Sumatra, Indonesia, known as a cultural center of the Minangkabau people and gateway to the scenic Minangkabau Highlands.
-
E.
Sawahlunto
Sawahlunto is a historic former coal-mining town in Indonesia known for its well-preserved colonial-era architecture and recognition as a UNESCO World Heritage Site.
- 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: Lumban Triple: [Laguna, hasMunicipality, Lumban]
Generated description
Lumban is a municipality in the Philippine province of Laguna known for its traditional hand-embroidered textiles and scenic lakeside setting along Laguna de Bay.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lumban Target entity description: Lumban is a municipality in the Philippine province of Laguna known for its traditional hand-embroidered textiles and scenic lakeside setting along Laguna de Bay.
-
A.
Bolango-Bulango
Bolango-Bulango is an Austronesian language spoken by the Bolango people in northern Sulawesi, Indonesia.
-
B.
Bulukumba
Bulukumba is a regency in South Sulawesi, Indonesia, known for its coastal landscapes, traditional boatbuilding, and Makassarese cultural heritage.
-
C.
Belawa
Belawa is a town and administrative district located within Wajo Regency in South Sulawesi, Indonesia.
-
D.
Batusangkar
Batusangkar is a historic town in West Sumatra, Indonesia, known as a cultural center of the Minangkabau people and gateway to the scenic Minangkabau Highlands.
-
E.
Sawahlunto
Sawahlunto is a historic former coal-mining town in Indonesia known for its well-preserved colonial-era architecture and recognition as a UNESCO World Heritage Site.
- 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_69ca82c0ef14819083713f4473dd847c |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4556b45c819089eb15ad027b036a |
completed | March 31, 2026, 3:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cced4e6efc8190a74aea4ab213298b |
completed | April 1, 2026, 10:02 a.m. |
| NEDg | Description generation | batch_69ccf09a952c8190ace6a9f0012ad90a |
completed | April 1, 2026, 10:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd055292088190927046793cec1c36 |
completed | April 1, 2026, 11:45 a.m. |
Created at: March 30, 2026, 5:38 p.m.