Triple
T4477898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terra (Expo 2020 mascot) |
E100055
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Terra |
E100055
|
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: Terra | Statement: [Terra (Expo 2020 mascot), name, Terra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terra Context triple: [Terra (Expo 2020 mascot), name, Terra]
-
A.
Terra
chosen
Terra is a sustainability-themed character created as one of the official mascots for Expo 2020 Dubai, symbolizing environmental awareness and ecological responsibility.
-
B.
La Terre
La Terre is a naturalist novel by Émile Zola that portrays the brutal lives, struggles, and moral decay of French peasants in the 19th century countryside.
-
C.
Earth
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.
-
D.
Verden
Verden is a historic town in Lower Saxony, Germany, known for its medieval cathedral and location along the Weser River.
-
E.
Tera
Tera is a West Chadic language spoken primarily in northeastern Nigeria by the Tera people.
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356d9d1d08190b3f9356eefd237dc |
completed | March 13, 2026, 12:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b63776ae008190a7668cddf1a42df3 |
completed | March 15, 2026, 4:37 a.m. |
Created at: March 12, 2026, 11:35 p.m.