Triple
T23362367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mon homme |
E593216
|
entity |
| Predicate | ratingFrance |
P152428
|
FINISHED |
| Object | restricted to adults |
—
|
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: restricted to adults | Statement: [Mon homme, ratingFrance, restricted to adults]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ratingFrance Context triple: [Mon homme, ratingFrance, restricted to adults]
-
A.
economicRankInFrance
Indicates the relative economic standing or ranking of an entity within the context of France’s economy.
-
B.
strengthFrance
Indicates a relationship where a level, measure, or attribute of strength is associated specifically with France.
-
C.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
D.
resultForFrance
Indicates the outcome or result of an event, action, or process specifically as it pertains to France.
-
E.
significanceForFrance
Indicates that something holds particular importance, impact, or relevance specifically in the context of France.
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a0aa0ed88190b0198cbfd1bcc59b |
completed | April 29, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69f061c7aaa48190a58ce93f87155ffc |
completed | April 28, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f0bd4a0e408190ad8916faf23562d9 |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 17, 2026, 5:30 p.m.