Triple
T22701230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hardoi district |
E561327
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Shahabad
Shahabad is a town in the Hardoi district of Uttar Pradesh, India, known as a local administrative and market center.
|
E1549852
|
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: Shahabad | Statement: [Hardoi district, hasSettlement, Shahabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shahabad Context triple: [Hardoi district, hasSettlement, Shahabad]
-
A.
Shahabad
Shahabad is a town in Uttar Pradesh, India, situated within the administrative boundaries of Rampur district.
-
B.
Shahabad
Shahabad is a town in the Kurukshetra district of Haryana, India, known historically by its former name Shahbad Markanda.
-
C.
Shamshabad
Shamshabad is a suburban area near Hyderabad in the Indian state of Telangana, known primarily for hosting the Rajiv Gandhi International Airport.
-
D.
Bandar Shahpur
Bandar Shahpur is a port city in southwestern Iran on the Persian Gulf that historically served as a key maritime and logistical hub, including during World War II supply routes.
-
E.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
- 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: Shahabad Triple: [Hardoi district, hasSettlement, Shahabad]
Generated description
Shahabad is a town in the Hardoi district of Uttar Pradesh, India, known as a local administrative and market center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shahabad Target entity description: Shahabad is a town in the Hardoi district of Uttar Pradesh, India, known as a local administrative and market center.
-
A.
Shahabad
Shahabad is a town in Uttar Pradesh, India, situated within the administrative boundaries of Rampur district.
-
B.
Shahabad
Shahabad is a town in the Kurukshetra district of Haryana, India, known historically by its former name Shahbad Markanda.
-
C.
Shamshabad
Shamshabad is a suburban area near Hyderabad in the Indian state of Telangana, known primarily for hosting the Rajiv Gandhi International Airport.
-
D.
Bandar Shahpur
Bandar Shahpur is a port city in southwestern Iran on the Persian Gulf that historically served as a key maritime and logistical hub, including during World War II supply routes.
-
E.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
- 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_69e2454e615481909c177440be559d2c |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178cb2f548190bfc6f050be7c795a |
completed | April 29, 2026, 3:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b7ed1f220819084322d245405829f |
completed | May 18, 2026, 9:04 p.m. |
| NEDg | Description generation | batch_6a0b7f823bc481909b646fdc338fdbb7 |
completed | May 18, 2026, 9:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b8054938c8190a1e873fa6ef00a8b |
completed | May 18, 2026, 9:10 p.m. |
Created at: April 17, 2026, 3:15 p.m.