Triple
T10223310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mancherial district |
E242635
|
entity |
| Predicate | hasUrbanCenter |
P2106
|
FINISHED |
| Object |
Luxettipet
Luxettipet is a town in the Mancherial district of Telangana, India, known as a local commercial and administrative center on the banks of the Godavari River.
|
E850511
|
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: Luxettipet | Statement: [Mancherial district, hasUrbanCenter, Luxettipet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luxettipet Context triple: [Mancherial district, hasUrbanCenter, Luxettipet]
-
A.
Lajoux
Lajoux is a small municipality in the Jura region of northwestern Switzerland, known for its rural setting and location on the Franches-Montagnes plateau.
-
B.
Plumette
Plumette is the feather-duster-turned-enchanted-bird maid and love interest of Lumière in Disney’s 2017 live-action adaptation of Beauty and the Beast.
-
C.
Lusser
Lusser is a German surname most notably associated with engineer Robert Lusser, known for his contributions to aeronautics and reliability engineering.
-
D.
Labouret
Labouret is a French surname associated with individuals such as Marie-Louise Élisabeth Labouret.
-
E.
Lux Esto
Lux Esto is the Latin motto of Kalamazoo College, traditionally translated as “Be Light.”
- 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: Luxettipet Triple: [Mancherial district, hasUrbanCenter, Luxettipet]
Generated description
Luxettipet is a town in the Mancherial district of Telangana, India, known as a local commercial and administrative center on the banks of the Godavari River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Luxettipet Target entity description: Luxettipet is a town in the Mancherial district of Telangana, India, known as a local commercial and administrative center on the banks of the Godavari River.
-
A.
Lajoux
Lajoux is a small municipality in the Jura region of northwestern Switzerland, known for its rural setting and location on the Franches-Montagnes plateau.
-
B.
Plumette
Plumette is the feather-duster-turned-enchanted-bird maid and love interest of Lumière in Disney’s 2017 live-action adaptation of Beauty and the Beast.
-
C.
Lusser
Lusser is a German surname most notably associated with engineer Robert Lusser, known for his contributions to aeronautics and reliability engineering.
-
D.
Labouret
Labouret is a French surname associated with individuals such as Marie-Louise Élisabeth Labouret.
-
E.
Lux Esto
Lux Esto is the Latin motto of Kalamazoo College, traditionally translated as “Be Light.”
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa8305e481908ee1fc1d9eda6fa0 |
completed | April 6, 2026, 12:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6a8457e9c819085f222bb002be892 |
completed | April 8, 2026, 7:11 p.m. |
| NEDg | Description generation | batch_69d6d00220ec81909d189e64eda2a28f |
completed | April 8, 2026, 10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6df44ad5481909100b596d2bf3b07 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 6, 2026, 11:10 a.m.