Triple
T10925081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warangal |
E258043
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object |
Kazipet
Kazipet is a major railway and educational hub in the Hanamkonda/Warangal urban area of Telangana, India.
|
E894117
|
NE FINISHED |
How this triple was built (4 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: Kazipet | Statement: [Warangal, nearbyCity, Kazipet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kazipet Context triple: [Warangal, nearbyCity, Kazipet]
-
A.
Laksar
Laksar is a town in the Haridwar district of Uttarakhand, India, known primarily as a significant railway junction connecting various parts of northern India.
-
B.
Kammala
Kammala was a historical figure known primarily as one of the children of Zhenjin, the Crown Prince of the Yuan dynasty and son of Kublai Khan.
-
C.
Karimabad
Karimabad is a neighborhood in Karachi, Pakistan, known for its bustling markets and central urban location within the city.
-
D.
Karimabad
Karimabad is a picturesque town in northern Pakistan’s Hunza region, known for its stunning mountain scenery, historic forts, and role as a popular base for trekkers and tourists.
-
E.
Saida Khera
Saida Khera is a village in Punjab, India, known in folklore as a setting linked to the legendary love story of Heer Ranjha.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kazipet Triple: [Warangal, nearbyCity, Kazipet]
Generated description
Kazipet is a major railway and educational hub in the Hanamkonda/Warangal urban area of Telangana, India.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kazipet Target entity description: Kazipet is a major railway and educational hub in the Hanamkonda/Warangal urban area of Telangana, India.
-
A.
Laksar
Laksar is a town in the Haridwar district of Uttarakhand, India, known primarily as a significant railway junction connecting various parts of northern India.
-
B.
Kammala
Kammala was a historical figure known primarily as one of the children of Zhenjin, the Crown Prince of the Yuan dynasty and son of Kublai Khan.
-
C.
Karimabad
Karimabad is a neighborhood in Karachi, Pakistan, known for its bustling markets and central urban location within the city.
-
D.
Karimabad
Karimabad is a picturesque town in northern Pakistan’s Hunza region, known for its stunning mountain scenery, historic forts, and role as a popular base for trekkers and tourists.
-
E.
Saida Khera
Saida Khera is a village in Punjab, India, known in folklore as a setting linked to the legendary love story of Heer Ranjha.
- F. None of above. chosen
Provenance (5 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708f7ab48190b60a4bb8fdb17c8e |
completed | April 9, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e217369b648190914c58db6f6e0200 |
completed | April 17, 2026, 11:19 a.m. |
| NEDg | Description generation | batch_69e21d8a2e6881909b33cbe4ab919315 |
completed | April 17, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e21eaa1e9881909f3b276e0ff0c511 |
completed | April 17, 2026, 11:51 a.m. |
Created at: April 8, 2026, 9:22 p.m.