Triple
T9875393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FRLEH |
E240059
|
entity |
| Predicate | hasFirstTwoLettersMeaning |
P27166
|
FINISHED |
| Object | France country code |
—
|
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: France country code | Statement: [FRLEH, hasFirstTwoLettersMeaning, France country code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFirstTwoLettersMeaning Context triple: [FRLEH, hasFirstTwoLettersMeaning, France country code]
-
A.
hasInitialLetters
chosen
Indicates that one entity’s initial letters or acronym are derived from or correspond to the other entity.
-
B.
hasPrefixMeaning
Indicates that one entity serves as a semantic prefix of another, contributing a specific meaning to the start of the second entity.
-
C.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
-
D.
hasLettersFor
Indicates that one entity possesses or contains written correspondence intended for another entity.
-
E.
secondLetterMatches
Indicates that the second character of one string or sequence is the same as the second character of another string or sequence.
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3f9d82c81908afb4977ce4e3e4a |
completed | April 2, 2026, 12:10 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:37 p.m.