Triple
T8046744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emblem of Iran |
E187572
|
entity |
| Predicate | elementCount |
P59623
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Emblem of Iran, elementCount, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: elementCount Context triple: [Emblem of Iran, elementCount, 5]
-
A.
elementCountDescription
Indicates a description of how many elements are present, often including both the count and contextual details about that quantity.
-
B.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
C.
numberOfEntries
chosen
Indicates the total count of individual items, records, or instances associated with a given entity or context.
-
D.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
E.
electronCount
Indicates the number of electrons associated with a given entity (such as an atom, ion, or molecule) in the described 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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f4d9ddc8190a7dcf85ed47ee6c3 |
completed | March 31, 2026, 3:28 a.m. |
| PD | Predicate disambiguation | batch_69cb049a1b9c8190811c396421ebf9c9 |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:24 p.m.