Triple
T14704525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marthe Rougon |
E345391
|
entity |
| Predicate | setInWorkLocation |
P1527
|
FINISHED |
| Object | Plassans |
E345390
|
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: Plassans | Statement: [Marthe Rougon, setInWorkLocation, Plassans]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Plassans Context triple: [Marthe Rougon, setInWorkLocation, Plassans]
-
A.
Plassans
chosen
Plassans is a fictional provincial town in southern France created by Émile Zola as a central setting in several of his Rougon-Macquart novels.
-
B.
Lessebo
Lessebo is a small locality and municipality in southern Sweden known for its traditional paper mill and glassmaking heritage.
-
C.
Paute
Paute is a small town in southern Ecuador known for its agricultural production and scenic Andean valley setting.
-
D.
Flesberg
Flesberg is a rural municipality in southeastern Norway known for its forests, traditional wooden architecture, and location in the Numedal valley.
-
E.
Karlaplan
Karlaplan is a prominent circular plaza and park with a central fountain in the Östermalm district of Stockholm, Sweden.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb6086c608190a66c64e23a3e002f |
completed | April 14, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf087ce8c819081a7186df67bcf1f |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:28 a.m.