Triple
T479228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rongorongo |
E9128
|
entity |
| Predicate | numberOfKnownInscriptions |
P12862
|
FINISHED |
| Object | about two dozen artifacts |
—
|
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: about two dozen artifacts | Statement: [Rongorongo, numberOfKnownInscriptions, about two dozen artifacts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfKnownInscriptions Context triple: [Rongorongo, numberOfKnownInscriptions, about two dozen artifacts]
-
A.
numberOfInscriptions
chosen
Indicates the total count of inscriptions associated with a given entity or object.
-
B.
numberOfStelae
Indicates the quantity of stelae associated with a given entity or context.
-
C.
mainContentOfInscriptions
Indicates that something represents the primary textual or symbolic content conveyed by a set of inscriptions.
-
D.
isInscribedOn
Indicates that text, symbols, or markings are written, carved, or otherwise permanently placed onto the surface of an object.
-
E.
officialLanguageOfInscriptions
Indicates the language officially used in the inscriptions associated with a particular entity.
- 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f056459881909749764cc4a7f9e8 |
completed | Feb. 28, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69a2edf1d5848190a7da27e2fddc136f |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.