Triple
T3234623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Les Rougon-Macquart |
E67822
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
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.
|
E339523
|
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: La Terre | Statement: [Les Rougon-Macquart, hasPart, La Terre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Terre Context triple: [Les Rougon-Macquart, hasPart, La Terre]
-
A.
Terra
Terra is a sustainability-themed character created as one of the official mascots for Expo 2020 Dubai, symbolizing environmental awareness and ecological responsibility.
-
B.
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.
-
C.
Verden
Verden is a historic town in Lower Saxony, Germany, known for its medieval cathedral and location along the Weser River.
-
D.
Maa
Maa is a Nilotic language spoken primarily by the Maasai people of Kenya and Tanzania.
-
E.
Tera
Tera is a West Chadic language spoken primarily in northeastern Nigeria by the Tera people.
- 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: La Terre Triple: [Les Rougon-Macquart, hasPart, La Terre]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: La Terre Target entity description: 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.
-
A.
Terra
Terra is a sustainability-themed character created as one of the official mascots for Expo 2020 Dubai, symbolizing environmental awareness and ecological responsibility.
-
B.
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.
-
C.
Verden
Verden is a historic town in Lower Saxony, Germany, known for its medieval cathedral and location along the Weser River.
-
D.
Maa
Maa is a Nilotic language spoken primarily by the Maasai people of Kenya and Tanzania.
-
E.
Tera
Tera is a West Chadic language spoken primarily in northeastern Nigeria by the Tera people.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaede0bdc8190a466f11bf2c50836 |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b277404f6c8190803cf67cc8423430 |
completed | March 12, 2026, 8:20 a.m. |
| NEDg | Description generation | batch_69b27844c6708190ac61f00a74a2ef27 |
completed | March 12, 2026, 8:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b27911ff1481908a36f279a871c510 |
completed | March 12, 2026, 8:28 a.m. |
Created at: March 8, 2026, 3:08 p.m.