Triple
T17347768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faro al Gianicolo |
E421727
|
entity |
| Predicate | lightingSymbolizes |
P107935
|
FINISHED |
| Object | Italian flag |
—
|
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: Italian flag | Statement: [Faro al Gianicolo, lightingSymbolizes, Italian flag]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lightingSymbolizes Context triple: [Faro al Gianicolo, lightingSymbolizes, Italian flag]
-
A.
lightingColor
Indicates the color or hue of the lighting applied to or associated with an entity.
-
B.
symbolismIn
chosen
Indicates that one entity functions as a symbol or representation within the context, meaning, or interpretive framework of another entity.
-
C.
roleAtIllumination
Indicates that an entity holds or held a specific role or position at the organization Illumination.
-
D.
symbolismFocus
Indicates that the primary emphasis of a work, element, or representation is on its symbolic meaning rather than its literal or functional aspects.
-
E.
symbolismOfWhite
Indicates the relationship in which the color white functions as a symbol conveying particular meanings, themes, or abstract concepts in a given context.
- 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a2a2ee48190976732e654a40053 |
completed | April 19, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69e3b02662d08190a07d0fb5c04b6f33 |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:44 a.m.