Triple
T2969953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Compagnon de la Libération |
E80255
|
entity |
| Predicate | totalCommunesRecipients |
P45298
|
FINISHED |
| Object | 5 |
—
|
LITERAL 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: 5 | Statement: [Compagnon de la Libération, totalCommunesRecipients, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalCommunesRecipients Context triple: [Compagnon de la Libération, totalCommunesRecipients, 5]
-
A.
totalRecipients
Indicates the total number of distinct entities that receive something in the context of the described relationship or action.
-
B.
servesCommune
Indicates that an entity provides services or functions in support of a particular commune or local municipality.
-
C.
hasRuralCommunes
Indicates that an entity possesses, includes, or is associated with one or more rural communes.
-
D.
provinceWithCommunity
Indicates that a given province contains, includes, or is associated with a particular community.
-
E.
numberOfParishes
Indicates the total count of parishes associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69ad8b14ffe881908ffed62f9595c867 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad997282b481909d078be0e70d9930 |
completed | March 8, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69ad960e71f8819088179d11248c6ed0 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad98379fac8190a4dfe530787703c9 |
completed | March 8, 2026, 3:39 p.m. |
Created at: March 8, 2026, 2:58 p.m.