Triple
T22168975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A History of the World in 100 Objects |
E547868
|
entity |
| Predicate | numberOfObjectsDiscussed |
P42152
|
FINISHED |
| Object | 100 |
—
|
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: 100 | Statement: [A History of the World in 100 Objects, numberOfObjectsDiscussed, 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfObjectsDiscussed Context triple: [A History of the World in 100 Objects, numberOfObjectsDiscussed, 100]
-
A.
numberOfDialogues
Indicates the total count of dialogues associated with or occurring between the referenced entities.
-
B.
numberOfCounts
chosen
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
C.
numberOfEntities
Indicates the total count of distinct entities involved in or associated with a given context or situation.
-
D.
numberDescribedAs
Indicates that a number is characterized, labeled, or referred to using a particular description or phrase.
-
E.
subjectCount
Indicates the number of subjects associated with or involved in a given entity or context.
- 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a6721e8819081d732df2691f4a4 |
completed | April 28, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69e71b41555881909b8e22718974d527 |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:34 p.m.