Triple
T8767593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tales of My Landlord |
E208374
|
entity |
| Predicate | hasPartCount |
P19198
|
FINISHED |
| Object | multiple volumes |
—
|
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: multiple volumes | Statement: [Tales of My Landlord, hasPartCount, multiple volumes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPartCount Context triple: [Tales of My Landlord, hasPartCount, multiple volumes]
-
A.
hasPart
Indicates that one entity is a component, segment, or constituent part of another entity.
-
B.
hasPartNumber
Indicates that one entity is assigned or associated with a specific part number used to identify it within a catalog, system, or inventory.
-
C.
hasComponentCount
chosen
Indicates that an entity is associated with a specific number of components it contains or comprises.
-
D.
hasNumberOfDivisions
Indicates the relationship that specifies how many divisions or subunits an entity possesses.
-
E.
numberOfPieces
Indicates the quantity of discrete parts or units into which something is divided or composed.
- 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_69ca835edb4481909b4aafb616dc5eb7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5eeae70c8190adc2bc2846da0fbf |
completed | March 31, 2026, 11:55 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1884bc8190a46e8308db31f7ab |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:41 p.m.