Triple
T11589254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khargone district |
E274834
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Segaon
Segaon is a small town located in the Khargone district of the Indian state of Madhya Pradesh.
|
E934113
|
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: Segaon | Statement: [Khargone district, containsTown, Segaon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Segaon Context triple: [Khargone district, containsTown, Segaon]
-
A.
Sugamo
Sugamo is a Tokyo neighborhood popularly known as the “Harajuku for old ladies,” famed for its Jizō-dōri shopping street and large elderly clientele.
-
B.
Asago
Asago is a city in northern Hyōgo Prefecture, Japan, known for its mountainous scenery, historic castle ruins, and hot spring resorts.
-
C.
Sendagaya
Sendagaya is a neighborhood in Tokyo known for its sports facilities, including the National Stadium, and its proximity to Shinjuku and Harajuku.
-
D.
Tsutsumi
Tsutsumi is a Japanese surname associated with several notable figures in business, politics, and the arts in Japan.
-
E.
Oimachi
Oimachi is a commercial and residential district in Tokyo known for its busy train hub, shopping streets, and convenient access to central Shinagawa and other parts of the city.
- 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: Segaon Triple: [Khargone district, containsTown, Segaon]
Generated description
Segaon is a small town located in the Khargone district of the Indian state of Madhya Pradesh.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Segaon Target entity description: Segaon is a small town located in the Khargone district of the Indian state of Madhya Pradesh.
-
A.
Sugamo
Sugamo is a Tokyo neighborhood popularly known as the “Harajuku for old ladies,” famed for its Jizō-dōri shopping street and large elderly clientele.
-
B.
Asago
Asago is a city in northern Hyōgo Prefecture, Japan, known for its mountainous scenery, historic castle ruins, and hot spring resorts.
-
C.
Sendagaya
Sendagaya is a neighborhood in Tokyo known for its sports facilities, including the National Stadium, and its proximity to Shinjuku and Harajuku.
-
D.
Tsutsumi
Tsutsumi is a Japanese surname associated with several notable figures in business, politics, and the arts in Japan.
-
E.
Oimachi
Oimachi is a commercial and residential district in Tokyo known for its busy train hub, shopping streets, and convenient access to central Shinagawa and other parts of the city.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d89463360c8190b91228c46bfe2e5f |
completed | April 10, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e71451f1388190b72d7b755d198999 |
completed | April 21, 2026, 6:08 a.m. |
| NEDg | Description generation | batch_69e720fc0f38819083bd15169f2ce4bb |
completed | April 21, 2026, 7:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e72353c19c8190b7a579e9af823872 |
completed | April 21, 2026, 7:12 a.m. |
Created at: April 8, 2026, 9:38 p.m.