Triple
T1964010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khan Noonien Singh |
E42647
|
entity |
| Predicate | origin |
P410
|
FINISHED |
| Object | Earth |
E687
|
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: Earth | Statement: [Khan Noonien Singh, origin, Earth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Earth Context triple: [Khan Noonien Singh, origin, Earth]
-
A.
Earth
chosen
Earth is the third planet from the Sun and the only known world to support life, characterized by vast oceans, diverse ecosystems, and a protective atmosphere.
-
B.
Terra
Terra is a sustainability-themed character created as one of the official mascots for Expo 2020 Dubai, symbolizing environmental awareness and ecological responsibility.
-
C.
Mars
Mars is the fourth planet from the Sun, a cold, desert-like world often called the "Red Planet" due to its iron-rich surface and a prime target in the search for past or present extraterrestrial life.
-
D.
Verden
Verden is a historic town in Lower Saxony, Germany, known for its medieval cathedral and location along the Weser River.
-
E.
The Green Planet
The Green Planet is a BBC nature documentary series that explores the hidden life, behavior, and ecological importance of plants around the world.
- 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_69a88711151c8190940b2572095059d7 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb3ada4148190ad830d4a3d7fd662 |
completed | March 7, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adfbd32eb88190a2069b6490b12e5d |
completed | March 8, 2026, 10:44 p.m. |
Created at: March 4, 2026, 7:36 p.m.