Triple
T23781406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vijaya Lakshmi |
E587823
|
entity |
| Predicate | alternativeSpacing |
P95533
|
FINISHED |
| Object | Vijayalakshmi |
—
|
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: Vijayalakshmi | Statement: [Vijaya Lakshmi, alternativeSpacing, Vijayalakshmi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alternativeSpacing Context triple: [Vijaya Lakshmi, alternativeSpacing, Vijayalakshmi]
-
A.
hasAlternativeSpacing
chosen
Indicates that an entity is associated with one or more alternative ways of spacing its characters or components compared to a primary or standard form.
-
B.
gapBetween
Indicates the spatial or temporal distance or separation that exists between two entities.
-
C.
hasStopSpacing
Indicates that there is a specified distance or interval between consecutive stops in a route or sequence.
-
D.
hasMeanSpacing
Indicates the average distance or interval between repeated or adjacent elements in a pattern, structure, or arrangement.
-
E.
usesBarAndSpaceWidths
Indicates that one entity determines or renders content using the specified widths of bars and spaces (typically in a barcode or similar encoded pattern).
- 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_69e2490f4ad48190b690878eec3596c6 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1c62bef608190b75afa6bf4024ae3 |
completed | April 29, 2026, 8:49 a.m. |
| PD | Predicate disambiguation | batch_69f155f79e34819080f9ddb972b34deb |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:16 p.m.