Triple
T594306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eurodollar futures |
E17345
|
entity |
| Predicate | quotedAs |
P4529
|
FINISHED |
| Object | 100 minus the annualized 3‑month USD LIBOR rate |
—
|
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: 100 minus the annualized 3‑month USD LIBOR rate | Statement: [Eurodollar futures, quotedAs, 100 minus the annualized 3‑month USD LIBOR rate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: quotedAs Context triple: [Eurodollar futures, quotedAs, 100 minus the annualized 3‑month USD LIBOR rate]
-
A.
notableQuote
Indicates that one entity is a significant or well-known quotation attributed to, recorded by, or strongly associated with another entity.
-
B.
interpretedAs
chosen
Indicates that something is understood, perceived, or taken to mean something else, often based on context or subjective judgment.
-
C.
alsoWrittenAs
Indicates that one entity has an alternative written form, spelling, or notation represented by the other entity.
-
D.
usedPhrase
Indicates that one entity employed or expressed a particular phrase in speech, writing, or another form of communication.
-
E.
describedByAuthorAs
Indicates that one entity is characterized, labeled, or portrayed in a particular way by an author.
- 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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bd15c5881909b59ed4c88687e7b |
completed | March 1, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69a494cd7d3c8190af008acf34a2293b |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.