Triple
T3116195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erode |
E65067
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Turmeric City
Turmeric City is a popular moniker for the South Indian city of Erode, renowned for its major role in turmeric cultivation and trade.
|
E326998
|
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: Turmeric City | Statement: [Erode, nickname, Turmeric City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Turmeric City Context triple: [Erode, nickname, Turmeric City]
-
A.
Silk City
Silk City is a historic nickname for Paterson, New Jersey, reflecting its past prominence as a major center of silk production in the United States.
-
B.
Silk City
Silk City is a popular nickname for Rajshahi, a major city in western Bangladesh historically renowned for its silk industry and fine silk products.
-
C.
Ochre City
Ochre City is a popular nickname for Marrakesh, referring to the Moroccan city's distinctive red and ochre-colored buildings and walls.
-
D.
Tinopolis
Tinopolis is the nickname of the Welsh town of Llanelli, historically renowned for its large tinplate industry.
-
E.
Apple City
Apple City is a nickname for Daegu, a major South Korean city historically renowned for its abundant apple production.
- 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: Turmeric City Triple: [Erode, nickname, Turmeric City]
Generated description
Turmeric City is a popular moniker for the South Indian city of Erode, renowned for its major role in turmeric cultivation and trade.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Turmeric City Target entity description: Turmeric City is a popular moniker for the South Indian city of Erode, renowned for its major role in turmeric cultivation and trade.
-
A.
Silk City
Silk City is a historic nickname for Paterson, New Jersey, reflecting its past prominence as a major center of silk production in the United States.
-
B.
Silk City
Silk City is a popular nickname for Rajshahi, a major city in western Bangladesh historically renowned for its silk industry and fine silk products.
-
C.
Ochre City
Ochre City is a popular nickname for Marrakesh, referring to the Moroccan city's distinctive red and ochre-colored buildings and walls.
-
D.
Tinopolis
Tinopolis is the nickname of the Welsh town of Llanelli, historically renowned for its large tinplate industry.
-
E.
Apple City
Apple City is a nickname for Daegu, a major South Korean city historically renowned for its abundant apple production.
- 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_69ad857fcc088190b0c4d45a5cde6f61 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada4e5d1488190a2ab199625fdf05d |
completed | March 8, 2026, 4:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2039d9d408190b01e45f0ced5ca3d |
completed | March 12, 2026, 12:06 a.m. |
| NEDg | Description generation | batch_69b20547c0008190ac2589f54111bad9 |
completed | March 12, 2026, 12:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b205bff1d88190a9bb212d9a607f18 |
completed | March 12, 2026, 12:16 a.m. |
Created at: March 8, 2026, 3:04 p.m.