Triple
T17170086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Five Laws of Library Science |
E416705
|
entity |
| Predicate | hasInfluencedConcept |
P19800
|
FINISHED |
| Object | information literacy |
—
|
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: information literacy | Statement: [Five Laws of Library Science, hasInfluencedConcept, information literacy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInfluencedConcept Context triple: [Five Laws of Library Science, hasInfluencedConcept, information literacy]
-
A.
hadInfluenceOn
Indicates that one entity affected, shaped, or contributed to the development, behavior, or characteristics of another entity.
-
B.
hasEnduringInfluenceOn
Indicates that one entity exerts a lasting, long-term impact on another entity’s state, development, or behavior.
-
C.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
D.
hasInfluenceOnDiscipline
chosen
Indicates that one entity exerts an effect, shaping force, or contributing impact on the development, direction, or state of a particular discipline.
-
E.
hasConcept
Indicates that an entity includes, embodies, or is associated with a particular concept.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f91831d88190b262227fc41c9067 |
completed | April 18, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69e3830d2a90819092386717dc56f0e8 |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:37 a.m.