Triple
T15029041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisbon fado |
E378291
|
entity |
| Predicate | typicalSettingFeature |
P111040
|
FINISHED |
| Object | dim lighting |
—
|
LITERAL FINISHED |
How this triple was built (2 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: dim lighting | Statement: [Lisbon fado, typicalSettingFeature, dim lighting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSettingFeature Context triple: [Lisbon fado, typicalSettingFeature, dim lighting]
-
A.
typicalFeatures
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
B.
featuresSituation
chosen
Indicates that a situation is characterized by, or has as one of its defining aspects, the specified feature or element.
-
C.
typicalSettingConsumed
Indicates the usual context or environment in which something is normally consumed.
-
D.
themeCharacteristic
Indicates that a characteristic, quality, or property is attributed to or associated with a particular theme.
-
E.
featureType
Indicates the specific kind or category of feature that characterizes or distinguishes an entity.
- F. None of above.
Provenance (3 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e0e8c88190ac6f5786b4d4040f |
completed | April 15, 2026, 12:12 a.m. |
| PD | Predicate disambiguation | batch_69de9a67cbc481909c19c2de57de4eb7 |
completed | April 14, 2026, 7:50 p.m. |
Created at: April 10, 2026, 2:58 a.m.