Triple
T35262100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Doomsday Machine (TOS episode) |
E1018394
|
entity |
| Predicate | featuresRank |
P206895
|
FINISHED |
| Object | Commodore |
—
|
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: Commodore | Statement: [The Doomsday Machine (TOS episode), featuresRank, Commodore]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresRank Context triple: [The Doomsday Machine (TOS episode), featuresRank, Commodore]
-
A.
featuresScoreBy
Indicates a scoring relationship where a feature or set of features is evaluated and assigned a score according to a specified criterion or entity.
-
B.
featuresScoringSystem
Indicates that one entity incorporates or provides a particular scoring or rating system as part of its functionality or design.
-
C.
scoreFeatures
Indicates assigning quantitative scores or evaluations to a set of features based on specified criteria or models.
-
D.
featuresInfluence
Indicates that certain features or characteristics have an effect on or contribute to changes in other features, outcomes, or behaviors.
-
E.
featuresSample
Indicates that an entity includes or presents a particular sample as one of its components or examples.
- 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_69f76de4be5c8190a51705c07612cac8 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:02 p.m.