Triple
T23461557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suchitepéquez Department |
E568988
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
San Lorenzo
San Lorenzo is a municipality in the Suchitepéquez Department of southwestern Guatemala, known for its agricultural economy and rural communities.
|
E1589746
|
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: San Lorenzo | Statement: [Suchitepéquez Department, hasMunicipality, San Lorenzo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Lorenzo Context triple: [Suchitepéquez Department, hasMunicipality, San Lorenzo]
-
A.
San Lorenzo
San Lorenzo is a historic church in the Italian town of Spello, known for its medieval architecture and religious significance.
-
B.
San Lorenzo
San Lorenzo is a rural municipality in Nicaragua’s central Boaco Department, known for its agricultural economy and small-town character.
-
C.
San Lorenzo
San Lorenzo is a town located in Peru's Loreto Department within the Amazon rainforest region.
-
D.
San Lorenzo
San Lorenzo is a coastal municipality on the island province of Guimaras in the Philippines, known for its rural communities and agricultural landscape.
-
E.
San Lorenzo
San Lorenzo is an upscale commercial and residential district in Makati, Metro Manila, known for its gated villages, shopping centers, and proximity to the central business area.
- 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: San Lorenzo Triple: [Suchitepéquez Department, hasMunicipality, San Lorenzo]
Generated description
San Lorenzo is a municipality in the Suchitepéquez Department of southwestern Guatemala, known for its agricultural economy and rural communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: San Lorenzo Target entity description: San Lorenzo is a municipality in the Suchitepéquez Department of southwestern Guatemala, known for its agricultural economy and rural communities.
-
A.
San Lorenzo
San Lorenzo is a rural municipality in Nicaragua’s central Boaco Department, known for its agricultural economy and small-town character.
-
B.
San Lorenzo
San Lorenzo is a municipality located in the Valle Department of Honduras, known for its coastal position on the Gulf of Fonseca and its role as a regional commercial and fishing center.
-
C.
San Lorenzo
San Lorenzo is a municipality in the central-eastern region of Puerto Rico known for its rural landscapes and small-town character.
-
D.
San Lorenzo
San Lorenzo is a coastal municipality on the island province of Guimaras in the Philippines, known for its rural communities and agricultural landscape.
-
E.
San Lorenzo
San Lorenzo is a town located in Peru's Loreto Department within the Amazon rainforest 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_69e2458ebd808190b3298163132cfb0b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a69bc200819096ed2baf25cdee4f |
completed | April 29, 2026, 6:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c825113588190a251450dc28f737b |
completed | May 19, 2026, 3:31 p.m. |
| NEDg | Description generation | batch_6a0ca6eee2f48190b4e03039c4c96abe |
completed | May 19, 2026, 6:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ca819f6dc8190852bb3c06768ac5a |
completed | May 19, 2026, 6:12 p.m. |
Created at: April 17, 2026, 5:54 p.m.