Triple
T32641282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zatoichi |
E834484
|
entity |
| Predicate | televisionSeriesCount |
P175493
|
FINISHED |
| Object | 1 long-running TV series starring Shintaro Katsu |
—
|
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: 1 long-running TV series starring Shintaro Katsu | Statement: [Zatoichi, televisionSeriesCount, 1 long-running TV series starring Shintaro Katsu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: televisionSeriesCount Context triple: [Zatoichi, televisionSeriesCount, 1 long-running TV series starring Shintaro Katsu]
-
A.
filmSeriesCount
Indicates the number of films that belong to a particular film series.
-
B.
televisionShow
Indicates that one entity is a television show associated with, or featured in relation to, another entity.
-
C.
numberOfTVFilms
Indicates the total count of television films associated with a given entity.
-
D.
timePeriodOfTelevisionSeries
Indicates the time span or era during which a television series is set or takes place.
-
E.
notableSeriesRun
Indicates that an entity is recognized for having a significant or distinguished run within a particular series or sequence of events.
- 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_69f3492e773c81908afc10651e46cad3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d210fc80819091ed8961aa2cddfb |
completed | May 3, 2026, 4:41 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe45554819089cbbd538d992132 |
completed | May 3, 2026, 4:32 a.m. |
| PDg | Predicate description generation | batch_69f6d16b79dc8190ab0d4657f2ef9a5b |
completed | May 3, 2026, 4:39 a.m. |
Created at: May 1, 2026, 1:07 a.m.