Triple
T17601138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novilara inscriptions |
E428702
|
entity |
| Predicate | hasReadingProblems |
P111042
|
FINISHED |
| Object | damaged letters |
—
|
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: damaged letters | Statement: [Novilara inscriptions, hasReadingProblems, damaged letters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReadingProblems Context triple: [Novilara inscriptions, hasReadingProblems, damaged letters]
-
A.
hasDyslexia
Indicates that an entity experiences dyslexia, a learning difficulty affecting reading, writing, or spelling abilities.
-
B.
hasLiteracyConcern
chosen
Indicates that an entity has an identified issue, risk, or challenge related to literacy skills or abilities.
-
C.
hasWordProblem
Indicates that an entity (such as a mathematical concept, operation, or topic) is associated with or can be expressed through a word problem.
-
D.
hasReading
Indicates that an entity is associated with a particular reading, such as a measured value, interpretation, or recorded observation.
-
E.
containsReading
Indicates that one entity includes or encompasses a particular reading (such as a measurement, value, or interpretation) within it.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c48dfc08190ba360e6082cffa87 |
completed | April 19, 2026, 5:46 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fff0348190b899a32da537eaca |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:51 a.m.