Triple
T5513029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Collège Jean-Zay |
E144611
|
entity |
| Predicate | isAcronymOrNamePattern |
P55987
|
FINISHED |
| Object | common school name in France |
—
|
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: common school name in France | Statement: [Collège Jean-Zay, isAcronymOrNamePattern, common school name in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAcronymOrNamePattern Context triple: [Collège Jean-Zay, isAcronymOrNamePattern, common school name in France]
-
A.
namePattern
Indicates that an entity’s name follows or matches a specified pattern or format.
-
B.
hasAbbreviationPattern
chosen
Indicates that there is a systematic abbreviation relationship between two strings, where one follows a recognizable pattern derived from the other.
-
C.
hasPattern
Indicates that one entity exhibits, follows, or is characterized by a specific recurring form, structure, or design defined by another entity.
-
D.
isAbbreviation
Indicates that one term is a shortened or abbreviated form of another term.
-
E.
notationPattern
Indicates a recurring way in which something is symbolically represented or written, such as a consistent style or structure of notation used for an entity or concept.
- F. None of above.
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_69c008f77ff88190b0cd50ca207295d1 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f599d0881909ce86fcc45d4d920 |
completed | March 22, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69c01b07bde08190b3933b96bdc70dd5 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:33 p.m.