Triple
T9346697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara |
E224907
|
entity |
| Predicate | hasBorough |
P300
|
FINISHED |
| Object | Montrouge |
E854634
|
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: Montrouge | Statement: [Barbara, hasBorough, Montrouge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montrouge Context triple: [Barbara, hasBorough, Montrouge]
-
A.
Montrouge
chosen
Montrouge is a suburban commune just south of Paris, France, known for its dense urban character and proximity to the capital.
-
B.
Levallois-Perret
Levallois-Perret is a densely populated suburban commune just northwest of central Paris, known for its residential character and proximity to the capital.
-
C.
Fontenay-aux-Roses
Fontenay-aux-Roses is a suburban commune in the southern outskirts of Paris, France, known for its residential character and historical ties to notable French artists and intellectuals.
-
D.
Villetaneuse
Villetaneuse is a suburban commune in the northern outskirts of Paris, France, known for its residential character and the presence of the Université Paris 13 campus.
-
E.
Aubervilliers
Aubervilliers is a densely populated suburban commune in the northeastern outskirts of Paris, known for its industrial past and cultural diversity.
- 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_69ca842993248190a79ab06968994b86 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4f0f4c4c8190a94c0d44ef349d7d |
completed | April 1, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef1243b14081909d07ab0ebb32cc68 |
completed | April 27, 2026, 7:37 a.m. |
Created at: March 30, 2026, 7:41 p.m.