Triple
T27165025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santiago |
E682757
|
entity |
| Predicate | learnsConcept |
P161927
|
FINISHED |
| Object | Maktub |
—
|
NE NERFINISHED |
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: Maktub | Statement: [Santiago, learnsConcept, Maktub]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learnsConcept Context triple: [Santiago, learnsConcept, Maktub]
-
A.
learnsConcept
chosen
Indicates that an entity acquires knowledge or understanding of a particular concept.
-
B.
learnsSkill
Indicates that an entity acquires or develops a particular skill through learning or practice.
-
C.
trainingConcept
Indicates that one entity serves as a concept, topic, or subject matter that is being taught or trained on in relation to another entity.
-
D.
usesLearningMechanism
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
-
E.
introducedConcept
Indicates that one entity is responsible for presenting, defining, or bringing a new concept into use or awareness for another entity or 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_69eefacf6e788190a75a64399d9e3109 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f62cd2ceb481908cd9fae52a542206 |
completed | May 2, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69f62c1762f881908c25e8f70ecd5041 |
completed | May 2, 2026, 4:53 p.m. |
Created at: April 27, 2026, 9:20 a.m.