Triple
T28197448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ladies Night |
E716482
|
entity |
| Predicate | featuresCover |
P196539
|
FINISHED |
| Object | Ladies Night by Kool & the Gang |
—
|
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: Ladies Night by Kool & the Gang | Statement: [Ladies Night, featuresCover, Ladies Night by Kool & the Gang]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCover Context triple: [Ladies Night, featuresCover, Ladies Night by Kool & the Gang]
-
A.
featuresHero
Indicates that something (such as a work, product, or story) prominently includes or centers around a particular hero as a main character or focus.
-
B.
cover
Indicates that one entity extends over, conceals, protects, or provides a surface or layer for another entity.
-
C.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
D.
featuresIn
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
-
E.
featuresCross
Indicates that one feature or element intersects or passes across another in space or structure.
- F. None of above. chosen
Provenance (4 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_69efd6b612f48190a72012b520afbd10 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69fe5c1a502081909d4024e514309c8e |
completed | May 8, 2026, 9:56 p.m. |
| PD | Predicate disambiguation | batch_69fe5a9df21c819087153f5d0bcaa987 |
completed | May 8, 2026, 9:50 p.m. |
| PDg | Predicate description generation | batch_69fe5c196f4081908f150d4cd6c528fa |
completed | May 8, 2026, 9:56 p.m. |
Created at: April 27, 2026, 10:28 p.m.