Triple
T2095105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nashik |
E32761
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
Gulshanabad
Gulshanabad is the former historical name of the Indian city now known as Nashik in Maharashtra.
|
E234880
|
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: Gulshanabad | Statement: [Nashik, formerName, Gulshanabad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gulshanabad Context triple: [Nashik, formerName, Gulshanabad]
-
A.
Nasirabad
Nasirabad is a town and administrative area located in the Balochistan region of present-day Pakistan.
-
B.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
-
C.
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.
-
D.
Faizabad
Faizabad is a historic city in the Indian state of Uttar Pradesh that once served as the capital of the former princely state of Oudh (Awadh).
-
E.
Badaro district
Badaro district is a vibrant residential and commercial neighborhood in Beirut, Lebanon, known for its cafés, nightlife, and proximity to major cultural and governmental institutions.
- 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: Gulshanabad Triple: [Nashik, formerName, Gulshanabad]
Generated description
Gulshanabad is the former historical name of the Indian city now known as Nashik in Maharashtra.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gulshanabad Target entity description: Gulshanabad is the former historical name of the Indian city now known as Nashik in Maharashtra.
-
A.
Nasirabad
Nasirabad is a town and administrative area located in the Balochistan region of present-day Pakistan.
-
B.
Wazirabad
Wazirabad is a city in the Gujranwala District of Punjab, Pakistan, known for its cutlery industry and strategic location near the Chenab River.
-
C.
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.
-
D.
Faizabad
Faizabad is a historic city in the Indian state of Uttar Pradesh that once served as the capital of the former princely state of Oudh (Awadh).
-
E.
Badaro district
Badaro district is a vibrant residential and commercial neighborhood in Beirut, Lebanon, known for its cafés, nightlife, and proximity to major cultural and governmental institutions.
- 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba99ddc48190bb2097b56efb7aca |
completed | March 7, 2026, 5:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae305cb77c819085c4f3eb2223f749 |
completed | March 9, 2026, 2:28 a.m. |
| NEDg | Description generation | batch_69ae30f6b7c4819080cb7cb7adc1f6d3 |
completed | March 9, 2026, 2:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae31871d408190a4ae64372660fa79 |
completed | March 9, 2026, 2:33 a.m. |
Created at: March 4, 2026, 7:43 p.m.