Triple
T9996213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mowgli |
E197207
|
entity |
| Predicate | associatedWithAnimal |
P51902
|
FINISHED |
| Object |
python Kaa
Python Kaa is a large, hypnotic snake from Rudyard Kipling’s "The Jungle Book," known as both a dangerous predator and, at times, an ally to the boy Mowgli.
|
E834589
|
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: python Kaa | Statement: [Mowgli, associatedWithAnimal, python Kaa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: python Kaa Context triple: [Mowgli, associatedWithAnimal, python Kaa]
-
A.
KAA
KAA is the state corporation responsible for managing and operating Kenya’s civil airports and airstrips.
-
B.
KAA
KAA is the commonly used abbreviation for K.A.A. Gent, a professional football club based in Ghent, Belgium.
-
C.
KCA
KCA is a nonprofit organization dedicated to providing life-saving HIV treatment, care, and support to children and families in underserved communities, particularly in Africa and India.
-
D.
KAU
KAU is the IATA airport code for Kauhava Air Base, a former military airfield in Kauhava, Finland.
-
E.
KAF
KAF is the Kenya Air Force, the aerial warfare branch of the Kenya Defence Forces responsible for defending Kenyan airspace and providing air support to military operations.
- 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: python Kaa Triple: [Mowgli, associatedWithAnimal, python Kaa]
Generated description
Python Kaa is a large, hypnotic snake from Rudyard Kipling’s "The Jungle Book," known as both a dangerous predator and, at times, an ally to the boy Mowgli.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: python Kaa Target entity description: Python Kaa is a large, hypnotic snake from Rudyard Kipling’s "The Jungle Book," known as both a dangerous predator and, at times, an ally to the boy Mowgli.
-
A.
KAA
KAA is the state corporation responsible for managing and operating Kenya’s civil airports and airstrips.
-
B.
KAA
KAA is the commonly used abbreviation for K.A.A. Gent, a professional football club based in Ghent, Belgium.
-
C.
KCA
KCA is a nonprofit organization dedicated to providing life-saving HIV treatment, care, and support to children and families in underserved communities, particularly in Africa and India.
-
D.
KAU
KAU is the IATA airport code for Kauhava Air Base, a former military airfield in Kauhava, Finland.
-
E.
KAF
KAF is the Kenya Air Force, the aerial warfare branch of the Kenya Defence Forces responsible for defending Kenyan airspace and providing air support to military operations.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcb9af6a88190942cc4991bd373c1 |
completed | April 2, 2026, 1:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2583bd6948190b15a97187e81d958 |
completed | April 5, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_69d2594b5e5081908f7cc4af4d10b4a8 |
completed | April 5, 2026, 12:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d25a30ad98819084dcd305e709c34d |
completed | April 5, 2026, 12:48 p.m. |
Created at: March 30, 2026, 8:50 p.m.