Triple
T7550116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ligue 1 Player of the Month |
E178507
|
entity |
| Predicate | hasTemporalGranularity |
P60332
|
FINISHED |
| Object | month |
—
|
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: month | Statement: [Ligue 1 Player of the Month, hasTemporalGranularity, month]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTemporalGranularity Context triple: [Ligue 1 Player of the Month, hasTemporalGranularity, month]
-
A.
timeTravelGranularity
Indicates the level of temporal precision or resolution at which time travel or time-based operations can occur between entities.
-
B.
hasTemporalClassification
Indicates a relationship where something is assigned or associated with a specific temporal category, period, or time-based classification.
-
C.
hasTemporalUse
Indicates that something is used, applicable, or valid only during a specific time or temporal interval.
-
D.
hasTimeDimension
Indicates that something possesses or is associated with a temporal aspect, such as duration, point in time, or time-based variation.
-
E.
hasDatePrecision
chosen
Indicates that a date value is associated with a specific level of granularity or exactness (such as year, month, day, or time).
- 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_69c69f2cbe08819088f9eb0c03ef529b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8b35ba481908e1e5bbf329daa33 |
completed | March 27, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69c6f4daad6c8190af2b8ae88d2c8cb7 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:49 p.m.