Triple
T1153662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Josef Mengele |
E23732
|
entity |
| Predicate | placeOfDeath |
P21
|
FINISHED |
| Object |
Bertioga
Bertioga is a coastal municipality in the state of São Paulo, Brazil, known for its beaches and Atlantic Forest landscapes.
|
E139027
|
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: Bertioga | Statement: [Josef Mengele, placeOfDeath, Bertioga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bertioga Context triple: [Josef Mengele, placeOfDeath, Bertioga]
-
A.
Loulé
Loulé is a historic market town and municipality in southern Portugal known for its traditional architecture, lively festivals, and role as a cultural and commercial center in the Algarve region.
-
B.
Olona
The Olona is a river in northern Italy that flows through the Lombardy region, including the city of Milan.
-
C.
Lihou
Lihou is a small tidal island off the west coast of Guernsey in the Channel Islands, known for its rich wildlife, historic priory ruins, and causeway access at low tide.
-
D.
Ile-Rousse
Île-Rousse is a coastal town and popular seaside resort in the Balagne region of northern Corsica, known for its red granite islets and sandy beaches.
-
E.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
- 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: Bertioga Triple: [Josef Mengele, placeOfDeath, Bertioga]
Generated description
Bertioga is a coastal municipality in the state of São Paulo, Brazil, known for its beaches and Atlantic Forest landscapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bertioga Target entity description: Bertioga is a coastal municipality in the state of São Paulo, Brazil, known for its beaches and Atlantic Forest landscapes.
-
A.
Loulé
Loulé is a historic market town and municipality in southern Portugal known for its traditional architecture, lively festivals, and role as a cultural and commercial center in the Algarve region.
-
B.
Olona
The Olona is a river in northern Italy that flows through the Lombardy region, including the city of Milan.
-
C.
Lihou
Lihou is a small tidal island off the west coast of Guernsey in the Channel Islands, known for its rich wildlife, historic priory ruins, and causeway access at low tide.
-
D.
Ile-Rousse
Île-Rousse is a coastal town and popular seaside resort in the Balagne region of northern Corsica, known for its red granite islets and sandy beaches.
-
E.
Saint-Tropez
Saint-Tropez is a coastal town on the French Riviera, famed as a glamorous Mediterranean resort and former artists’ haven.
- 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc8e9cb481908a528a828b21d497 |
completed | March 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac830d57e0819086fd19e032a589cd |
completed | March 7, 2026, 7:57 p.m. |
| NEDg | Description generation | batch_69ac837e06cc8190b0da34646fa78c0c |
completed | March 7, 2026, 7:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac84309acc8190aac6c3c78246b352 |
completed | March 7, 2026, 8:01 p.m. |
Created at: March 1, 2026, 7:44 p.m.