Triple
T7905395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Christmas Carol |
E183562
|
entity |
| Predicate | numberOfStaves |
P79696
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [A Christmas Carol, numberOfStaves, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStaves Context triple: [A Christmas Carol, numberOfStaves, 5]
-
A.
numberOfOfficialStanzas
Indicates the total count of officially recognized stanzas associated with an entity, such as a song, poem, or anthem.
-
B.
usesInterpretiveStaff
Indicates that an entity relies on interpretive staff (such as guides, educators, or docents) to convey information, context, or explanations about something to an audience.
-
C.
numberOfStages
Indicates the total count of distinct stages or phases associated with a given process, event, or entity.
-
D.
numberOfStrings
Indicates the quantity of strings associated with or contained by an entity.
-
E.
numberOfStanzasInOriginalPoem
Indicates the total count of stanzas contained in the poem’s original version.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a56c9f0819094dc87fe55a8823e |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7882b048190baa333af9f698590 |
completed | March 30, 2026, 10:22 p.m. |
Created at: March 30, 2026, 5:03 p.m.