Triple
T31114506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riq‘a (classical) |
E793043
|
entity |
| Predicate | letterformFeature |
P150002
|
FINISHED |
| Object | reduced loops |
—
|
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: reduced loops | Statement: [Riq‘a (classical), letterformFeature, reduced loops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: letterformFeature Context triple: [Riq‘a (classical), letterformFeature, reduced loops]
-
A.
hasLetterforms
chosen
Indicates a relationship where one entity possesses or includes specific letterforms as part of its written or typographic representation.
-
B.
spanFeature
Indicates a relationship where a feature or characteristic extends across or covers a specified span or interval.
-
C.
hasLigatures
Indicates that one writing system, font, or text includes combined character forms (ligatures) that join two or more individual glyphs into a single symbol.
-
D.
FontographerType
Indicates a relationship where an entity is classified as a type or category within the Fontographer system or schema.
-
E.
inscriptionFeature
Indicates that one entity bears or contains an inscribed element or marking that is treated as a notable feature in relation to another 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_69f224d0a7688190af3fe3e6e26d01ed |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69c234d648190a243fb2b107136a9 |
completed | May 3, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69f69665cd9c819088c388fc82fec42e |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 29, 2026, 9:04 p.m.