Triple
T2668903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ahu (ceremonial platforms) |
E55701
|
entity |
| Predicate | count |
P41575
|
FINISHED |
| Object | over 300 examples on Rapa Nui |
—
|
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 300 examples on Rapa Nui | Statement: [ahu (ceremonial platforms), count, over 300 examples on Rapa Nui]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: count Context triple: [ahu (ceremonial platforms), count, over 300 examples on Rapa Nui]
-
A.
dropCount
Indicates the number of times an entity has been dropped or caused to drop something.
-
B.
bookmarkCount
Indicates the number of times an item has been bookmarked by users.
-
C.
registerCount
Indicates the number of registers associated with or allocated to a given entity in a system.
-
D.
titleCount
Indicates the number of distinct titles associated with an entity within a given context.
-
E.
arrowCount
Indicates the number of arrows associated with or involved in a given entity or interaction.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd98bacf48190b5633f1a3ec8f0cf |
completed | March 7, 2026, 7:53 a.m. |
| PD | Predicate disambiguation | batch_69abd8190ad481908f3e14ac84d0940a |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abd8f98c348190a68064c565589459 |
completed | March 7, 2026, 7:51 a.m. |
Created at: March 6, 2026, 9:54 p.m.