Triple
T18538356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PIN code system of India |
E453022
|
entity |
| Predicate | regionGranularity |
P109501
|
FINISHED |
| Object | from large zones to individual post offices |
—
|
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: from large zones to individual post offices | Statement: [PIN code system of India, regionGranularity, from large zones to individual post offices]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionGranularity Context triple: [PIN code system of India, regionGranularity, from large zones to individual post offices]
-
A.
granularityLevel
chosen
Indicates the degree of detail or resolution at which something is specified, measured, or analyzed within a given context.
-
B.
reservationGranularity
Indicates the level of detail or unit (such as time, quantity, or capacity) at which a reservation can be specified or managed.
-
C.
translationGranularity
Indicates the level of detail or segmentation at which a translation is produced or aligned between source and target content.
-
D.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
E.
timeGranularity
Indicates the level of temporal detail or precision at which an event, measurement, or relationship is defined (e.g., seconds, days, months).
- 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e534030bd88190b25b95305a12a0c1 |
completed | April 19, 2026, 7:58 p.m. |
| PD | Predicate disambiguation | batch_69e469e0025c81908f16ed4f922674af |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 11:37 a.m.