Triple
T3087932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lao kip |
E64418
|
entity |
| Predicate | rarelyUsedBanknotes |
P45809
|
FINISHED |
| Object | 500 kip |
—
|
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: 500 kip | Statement: [Lao kip, rarelyUsedBanknotes, 500 kip]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rarelyUsedBanknotes Context triple: [Lao kip, rarelyUsedBanknotes, 500 kip]
-
A.
frequentlyUsedBanknotes
Indicates that the referenced banknotes are commonly or regularly used in transactions within a given context or system.
-
B.
hasBanknotes
Indicates that an entity possesses or contains one or more banknotes.
-
C.
issuesBanknotes
Indicates that an entity (typically a central bank or monetary authority) produces and puts banknotes into official circulation as legal tender.
-
D.
rarelyUsedCoins
Indicates that the coins in question are infrequently or almost never used in transactions or everyday circulation.
-
E.
typeOfBanknotes
Indicates a relationship where one entity specifies the kind or category of banknotes associated with 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_69ad857c97d88190b26f9b1c90839c77 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada209fd24819088d887de0a4158f4 |
completed | March 8, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69ad9ded78f881908be6fc0fb7c35764 |
completed | March 8, 2026, 4:03 p.m. |
| PDg | Predicate description generation | batch_69ada0f6fef48190b13898be383a246b |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 8, 2026, 3:03 p.m.