Triple
T16062379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hisar Lok Sabha constituency |
E389642
|
entity |
| Predicate | hasAssemblySegment |
P121483
|
FINISHED |
| Object | Narnaund |
E394996
|
NE 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: Narnaund | Statement: [Hisar Lok Sabha constituency, hasAssemblySegment, Narnaund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Narnaund Context triple: [Hisar Lok Sabha constituency, hasAssemblySegment, Narnaund]
-
A.
Narnaund
chosen
Narnaund is a prominent town in the northern Indian state of Haryana, known for its agricultural economy and role as a local commercial hub.
-
B.
Umarkhed
Umarkhed is a town in the Yavatmal district of Maharashtra, India, known as a local commercial and administrative center for the surrounding rural region.
-
C.
Nakodar
Nakodar is a prominent town in the Indian state of Punjab, known for its historical significance and cultural heritage within the Jalandhar region.
-
D.
Randhawa
Randhawa is an Indian-origin Punjabi surname notably borne by American politician Nikki Haley.
-
E.
Ishwarpur
Ishwarpur is a municipality-level city located in Nepal’s Madhesh Province.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e21a00f6808190a60939ef7ce727a7 |
completed | April 17, 2026, 11:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff798c2a48190b6eccd476a0a396f |
completed | May 10, 2026, 3:12 a.m. |
Created at: April 10, 2026, 4:57 a.m.