Triple
T14937881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morbi district |
E372442
|
entity |
| Predicate | hasNotableTown |
P14082
|
FINISHED |
| Object | Tankara |
E1129807
|
NE FINISHED |
How this triple was built (2 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: Tankara | Statement: [Morbi district, hasNotableTown, Tankara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tankara Context triple: [Morbi district, hasNotableTown, Tankara]
-
A.
Tankara
chosen
Tankara is a town in the Morbi district of Gujarat, India, known as the birthplace of the social reformer Swaminarayan.
-
B.
Tagakaolo
Tagakaolo is an indigenous ethnolinguistic group in the southern Philippines, primarily in parts of Davao and Sarangani, known for its distinct Austronesian language and cultural traditions.
-
C.
Datooga
Datooga is a Southern Nilotic language spoken primarily by the Datooga people of north-central Tanzania.
-
D.
Comala
Comala is the haunting, ghostly Mexican town that serves as the central setting of Juan Rulfo’s novel "Pedro Páramo."
-
E.
Comala
Comala is a town in the Mexican state of Colima, known for its picturesque colonial architecture and proximity to the active Volcán de Fuego.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded64904d88190b6b4140da8e8199d |
completed | April 15, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe968d71dc81909b76551f9cd9ebab |
completed | May 9, 2026, 2:06 a.m. |
Created at: April 10, 2026, 2:38 a.m.