Triple
T19922416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MP-17 |
E478829
|
entity |
| Predicate | RTOCode |
P58492
|
FINISHED |
| Object | 17 |
—
|
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: 17 | Statement: [MP-17, RTOCode, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: RTOCode Context triple: [MP-17, RTOCode, 17]
-
A.
stateCapitalRTOCodeExample
Indicates that a particular RTO (Regional Transport Office) code is used as an example associated with the state capital in a given context.
-
B.
icaoStateCode
Indicates the association between an entity (such as an aircraft, airline, or airport) and the ICAO-assigned state or country code under whose jurisdiction it falls.
-
C.
regionCodeType
Indicates the classification or format type used for a given region code within a coding or identification system.
-
D.
zoneCode
chosen
Indicates that an entity is associated with, or assigned to, a specific geographic or administrative zone identified by a code.
-
E.
raceCode
Indicates the classification of an entity according to a standardized race or ethnicity code.
- 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659c6919c8190a96106532580b6b6 |
completed | April 20, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69e537f070b481908958e0e5911dcdc1 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:53 p.m.