Triple
T479195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rano Raraku |
E9127
|
entity |
| Predicate | approximateNumberOfMoai |
P14789
|
FINISHED |
| Object | over 390 moai |
—
|
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 390 moai | Statement: [Rano Raraku, approximateNumberOfMoai, over 390 moai]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfMoai Context triple: [Rano Raraku, approximateNumberOfMoai, over 390 moai]
-
A.
numberOfStelae
Indicates the quantity of stelae associated with a given entity or context.
-
B.
approximateMass
Indicates that one entity has a mass value that is an estimate or close approximation of the mass of another entity.
-
C.
approximateStellarCount
Indicates an estimated or rough number of stars associated with a given astronomical object or region.
-
D.
estimatedNumberOfPaintings
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
E.
approximateSize
Indicates that one entity has a size that is roughly or approximately equal to the size of another entity.
- 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_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. |
| PDg | Predicate description generation | batch_69a2ef4030608190b39852b347a505ca |
completed | Feb. 28, 2026, 1:36 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.