Triple
T37515073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Templar |
E932613
|
entity |
| Predicate | hasAttributeFocus |
P165265
|
FINISHED |
| Object | Strength |
—
|
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: Strength | Statement: [Templar, hasAttributeFocus, Strength]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAttributeFocus Context triple: [Templar, hasAttributeFocus, Strength]
-
A.
hasRDFocus
Indicates that something has a specific region of interest or focal area within an image, scene, or dataset that is being emphasized or analyzed.
-
B.
hasAccessibilityFocus
Indicates that a user interface element is currently the primary target of accessibility tools, such as screen readers or keyboard navigation, receiving focused attention for interaction.
-
C.
hasValueFocus
chosen
Indicates that a particular value or data item is the primary focus or point of emphasis within a given context or relationship.
-
D.
hasVisualFocus
Indicates that one entity is currently directing its visual attention or gaze toward another entity.
-
E.
hasCharacterFocus
Indicates that a work, scene, or segment centers primarily on a particular character’s experiences, perspective, or development.
- 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_69f76ec730988190b5aa4f9cb9afd518 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba5eec0448190a5e6f0c43fdcd0e3 |
completed | May 6, 2026, 8:34 p.m. |
| PD | Predicate disambiguation | batch_69fba34edd548190bfa980e6e16e0a88 |
completed | May 6, 2026, 8:23 p.m. |
Created at: May 3, 2026, 4:17 p.m.