Triple
T16062380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hisar Lok Sabha constituency |
E389642
|
entity |
| Predicate | hasAssemblySegment |
P121483
|
FINISHED |
| Object | Hansi |
E389633
|
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: Hansi | Statement: [Hisar Lok Sabha constituency, hasAssemblySegment, Hansi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hansi Context triple: [Hisar Lok Sabha constituency, hasAssemblySegment, Hansi]
-
A.
Hansi
chosen
Hansi is a historic town in the Hisar district of Haryana, India, known for its ancient forts and archaeological significance.
-
B.
Hans
Hans is a masculine given name of Germanic origin commonly used in Germanic and Scandinavian countries.
-
C.
Günther
Günther is the zoologist who first formally described the impressed tortoise species Manouria impressa.
-
D.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
E.
Hermann
Hermann is the obsessive, tormented protagonist of Alexander Pushkin’s novella "The Queen of Spades," whose fixation on a secret winning card formula leads to his psychological and moral downfall.
- 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_69ffe47ca9748190be24a490c3cf0e8c |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:57 a.m.