Triple
T120042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doctor Who |
E2424
|
entity |
| Predicate | hasCompanionType |
P121
|
FINISHED |
| Object | human companions |
—
|
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: human companions | Statement: [Doctor Who, hasCompanionType, human companions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCompanionType Context triple: [Doctor Who, hasCompanionType, human companions]
-
A.
hasCollectionType
Indicates that an entity is associated with or organized under a specific type or category of collection.
-
B.
hasMemberType
chosen
Indicates that an entity includes or is associated with members belonging to a specified type or category.
-
C.
hasTypeName
Indicates that an entity is associated with a specific type name used to classify or identify its kind.
-
D.
hasStructureType
Indicates that an entity possesses or is classified by a specific structural type or configuration.
-
E.
hasModelType
Indicates that an entity is associated with or classified under a specific model type.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a258fd278481908ad4498e03f38e2f |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a25647fe6081908cb0405266d35ff5 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.