Triple
T37808909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 100 Questions |
E942580
|
entity |
| Predicate | originallyOrderedEpisodes |
P204397
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [100 Questions, originallyOrderedEpisodes, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originallyOrderedEpisodes Context triple: [100 Questions, originallyOrderedEpisodes, 13]
-
A.
statusOfOriginalEpisodes
Indicates the current condition, availability, or release state of the original episodes in relation to a work or series.
-
B.
hasEpisodeCountOrder
Indicates the ordered position of an entity within a sequence based on its episode count.
-
C.
originallyPlannedNumberOfEpisodes
Indicates the total number of episodes that were initially intended or planned for a series or program before any changes occurred.
-
D.
intendedEpisodes
Indicates that one entity is planned or designated to appear in, be used for, or be associated with specific episodes of another entity (such as a series or program).
-
E.
originalOrder
Indicates that the entities are arranged or referenced according to their initial, unaltered sequence or configuration.
- 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_69f76ee8104c8190ab17133ccd8f86e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.