Triple
T15606705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sertã |
E375176
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Pedrógão Pequeno
Pedrógão Pequeno is a small village in central Portugal known for its scenic setting near the Zêzere River and its historic bridge and dam.
|
E1174908
|
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: Pedrógão Pequeno | Statement: [Sertã, hasPart, Pedrógão Pequeno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pedrógão Pequeno Context triple: [Sertã, hasPart, Pedrógão Pequeno]
-
A.
Pedrógão Grande
Pedrógão Grande is a small municipality in central Portugal known for its forested landscapes and the devastating 2017 wildfires that caused significant loss of life and damage.
-
B.
Pedrógão
Pedrógão is a civil parish located within the municipality of Vidigueira in Portugal’s Alentejo region.
-
C.
Dão-Lafões
Dão-Lafões is a subregion in central Portugal known for its mountainous landscapes, thermal spas, and production of Dão wines.
-
D.
Sernancelhe
Sernancelhe is a municipality in northern Portugal known for its historic granite architecture, religious heritage, and scenic rural landscapes.
-
E.
Mosteiros
Mosteiros is a coastal municipality on the island of Fogo in Cape Verde, known for its volcanic landscapes, coffee production, and black-sand beaches.
- 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: Pedrógão Pequeno Triple: [Sertã, hasPart, Pedrógão Pequeno]
Generated description
Pedrógão Pequeno is a small village in central Portugal known for its scenic setting near the Zêzere River and its historic bridge and dam.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pedrógão Pequeno Target entity description: Pedrógão Pequeno is a small village in central Portugal known for its scenic setting near the Zêzere River and its historic bridge and dam.
-
A.
Pedrógão Grande
Pedrógão Grande is a small municipality in central Portugal known for its forested landscapes and the devastating 2017 wildfires that caused significant loss of life and damage.
-
B.
Pedrógão
Pedrógão is a civil parish located within the municipality of Vidigueira in Portugal’s Alentejo region.
-
C.
Dão-Lafões
Dão-Lafões is a subregion in central Portugal known for its mountainous landscapes, thermal spas, and production of Dão wines.
-
D.
Sernancelhe
Sernancelhe is a municipality in northern Portugal known for its historic granite architecture, religious heritage, and scenic rural landscapes.
-
E.
Mosteiros
Mosteiros is a coastal municipality on the island of Fogo in Cape Verde, known for its volcanic landscapes, coffee production, and black-sand beaches.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e7ec08c8190b3842cf3043aea27 |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff875bb0808190a6a4e3b47b524689 |
completed | May 9, 2026, 7:13 p.m. |
| NEDg | Description generation | batch_69ff8827e5e0819084e12bfd546ed215 |
completed | May 9, 2026, 7:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff88cfbe388190b20c426b4c745f92 |
completed | May 9, 2026, 7:19 p.m. |
Created at: April 10, 2026, 4:13 a.m.