Triple
T2898241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IFIP |
E62592
|
entity |
| Predicate | hasAcronymExpansionLanguage |
P43870
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [IFIP, hasAcronymExpansionLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAcronymExpansionLanguage Context triple: [IFIP, hasAcronymExpansionLanguage, English]
-
A.
acronymExpansion
Indicates that one term is an acronym whose letters stand for the words in another, longer expression.
-
B.
hasAcronymOrigin
Indicates that an acronym is derived from or originates from a specific longer expression or name.
-
C.
hasSecondWordOfExpandedForm
Indicates that the second word in the fully expanded (non-abbreviated) form of one entity is related to or associated with another entity.
-
D.
hasFirstWordOfExpandedForm
Indicates that one entity is the first word in the fully expanded (non-abbreviated) form of another entity.
-
E.
isLinguaFrancaOf
Indicates that a language serves as a common medium of communication between speakers of different native languages within a particular region, community, or context.
- 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe08fe3248190a6bb7de2a2c317b1 |
completed | March 7, 2026, 8:23 a.m. |
| PD | Predicate disambiguation | batch_69abdd17bcdc8190aa47274a50ba4ad4 |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abde0f4c648190b9812e64f30c39da |
completed | March 7, 2026, 8:13 a.m. |
Created at: March 6, 2026, 10:10 p.m.