Triple
T8219821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dazu Rock Carvings |
E192028
|
entity |
| Predicate | numberOfInscriptionCharacters |
P32078
|
FINISHED |
| Object | over 100000 |
—
|
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: over 100000 | Statement: [Dazu Rock Carvings, numberOfInscriptionCharacters, over 100000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInscriptionCharacters Context triple: [Dazu Rock Carvings, numberOfInscriptionCharacters, over 100000]
-
A.
numberOfInscriptions
Indicates the total count of inscriptions associated with a given entity or object.
-
B.
hasNumberOfNamesInscribed
Indicates the quantity of distinct names that are inscribed on a given entity.
-
C.
numberOfCharacters
chosen
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
D.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
E.
numberOfStelae
Indicates the quantity of stelae associated with 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_69ca82c9a8ac81908b011c38698456e4 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb7772b76c8190b1952650c736eb91 |
completed | March 31, 2026, 7:27 a.m. |
| PD | Predicate disambiguation | batch_69cb36af41e081909dee92b9bc4947f1 |
completed | March 31, 2026, 2:51 a.m. |
Created at: March 30, 2026, 5:45 p.m.