Triple
T2806409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Picasso Museum |
E54062
|
entity |
| Predicate | occupies |
P2574
|
FINISHED |
| Object |
Palau Meca
Palau Meca is a historic palace in Barcelona’s Gothic Quarter that forms part of the architectural complex housing the Picasso Museum.
|
E299312
|
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: Palau Meca | Statement: [Picasso Museum, occupies, Palau Meca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Palau Meca Context triple: [Picasso Museum, occupies, Palau Meca]
-
A.
Malaka
Malaka is the ancient Phoenician and later Roman name for the city now known as Málaga in southern Spain.
-
B.
Petit Palau
Petit Palau is an intimate, modern auditorium within Barcelona’s Palau de la Música Catalana complex, designed for smaller-scale concerts and cultural events.
-
C.
Kainan
Kainan is a coastal city in central Wakayama Prefecture, Japan, known for its traditional industries and scenic seaside setting.
-
D.
Bantia
Bantia was an ancient Oscan-speaking city in southern Italy, notable for yielding important inscriptions that illuminate the Oscan language and Italic legal traditions.
-
E.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
- 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: Palau Meca Triple: [Picasso Museum, occupies, Palau Meca]
Generated description
Palau Meca is a historic palace in Barcelona’s Gothic Quarter that forms part of the architectural complex housing the Picasso Museum.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Palau Meca Target entity description: Palau Meca is a historic palace in Barcelona’s Gothic Quarter that forms part of the architectural complex housing the Picasso Museum.
-
A.
Malaka
Malaka is the ancient Phoenician and later Roman name for the city now known as Málaga in southern Spain.
-
B.
Petit Palau
Petit Palau is an intimate, modern auditorium within Barcelona’s Palau de la Música Catalana complex, designed for smaller-scale concerts and cultural events.
-
C.
Kainan
Kainan is a coastal city in central Wakayama Prefecture, Japan, known for its traditional industries and scenic seaside setting.
-
D.
Bantia
Bantia was an ancient Oscan-speaking city in southern Italy, notable for yielding important inscriptions that illuminate the Oscan language and Italic legal traditions.
-
E.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
- 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_69ab49dcee188190b5c6eca9ae9e3469 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde165a448190aa2728ec074daf88 |
completed | March 7, 2026, 8:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc676d03081908986026dfe5fc852 |
completed | March 10, 2026, 7:21 a.m. |
| NEDg | Description generation | batch_69afc7e1e0708190a1e87212435029f6 |
completed | March 10, 2026, 7:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc8470cc081908241d23975181aa0 |
completed | March 10, 2026, 7:29 a.m. |
Created at: March 6, 2026, 9:59 p.m.