Triple
T11128415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FMM Sines – Festival Músicas do Mundo |
E263208
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
FMM Sines
FMM Sines is a renowned world music festival held annually in Sines, Portugal, showcasing diverse global artists and musical traditions.
|
E905774
|
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: FMM Sines | Statement: [FMM Sines – Festival Músicas do Mundo, alsoKnownAs, FMM Sines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FMM Sines Context triple: [FMM Sines – Festival Músicas do Mundo, alsoKnownAs, FMM Sines]
-
A.
SFM
SFM is the station code for San Francisco's 4th and King Street Caltrain terminal, a major commuter rail hub in the city.
-
B.
SMF
SMF (System Management Facilities) is an IBM z/OS component that collects and records system and workload performance data for monitoring, accounting, and capacity planning.
-
C.
SMF
SMF is the three-letter IATA airport code for Sacramento International Airport, the primary commercial airport serving California’s capital city.
-
D.
SFS
SFS is a renowned Georgetown University school specializing in international affairs, diplomacy, and global policy education.
-
E.
SFS
SFS is a spatial feature standard that defines how geographic features and their properties are modeled and accessed in geospatial information systems.
- 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: FMM Sines Triple: [FMM Sines – Festival Músicas do Mundo, alsoKnownAs, FMM Sines]
Generated description
FMM Sines is a renowned world music festival held annually in Sines, Portugal, showcasing diverse global artists and musical traditions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FMM Sines Target entity description: FMM Sines is a renowned world music festival held annually in Sines, Portugal, showcasing diverse global artists and musical traditions.
-
A.
SFM
SFM is the station code for San Francisco's 4th and King Street Caltrain terminal, a major commuter rail hub in the city.
-
B.
SMF
SMF (System Management Facilities) is an IBM z/OS component that collects and records system and workload performance data for monitoring, accounting, and capacity planning.
-
C.
SMF
SMF is the three-letter IATA airport code for Sacramento International Airport, the primary commercial airport serving California’s capital city.
-
D.
SFS
SFS is a renowned Georgetown University school specializing in international affairs, diplomacy, and global policy education.
-
E.
SFS
SFS is a spatial feature standard that defines how geographic features and their properties are modeled and accessed in geospatial information systems.
- 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_69d6aa9c0ba08190bbd19c217489b755 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e830e804819097fcc3826d84dab8 |
completed | April 9, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d8b3ce4819082f9efcf8dec4f1a |
completed | April 19, 2026, 1:19 a.m. |
| NEDg | Description generation | batch_69e42f3eaa0c819095e5af20b910d979 |
completed | April 19, 2026, 1:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4377d20ac8190b0bb810d4159ac4d |
completed | April 19, 2026, 2:01 a.m. |
Created at: April 8, 2026, 9:28 p.m.