Triple
T9544069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FTSE Emerging Index |
E230236
|
entity |
| Predicate | numberOfConstituentsType |
P89716
|
FINISHED |
| Object | variable |
—
|
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: variable | Statement: [FTSE Emerging Index, numberOfConstituentsType, variable]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfConstituentsType Context triple: [FTSE Emerging Index, numberOfConstituentsType, variable]
-
A.
numberOfConstituents
Indicates the total count of individual components or members that make up a larger whole or group.
-
B.
typicalConstituentType
Indicates the usual or characteristic type of component that typically makes up or forms part of something.
-
C.
previousNumberOfConstituents
Indicates the number of constituents an entity had at an earlier point in time, before its current state.
-
D.
majorConstituentType
Indicates the type or category of a primary or dominant component that makes up a larger whole.
-
E.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
- 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_69ca847c70b8819088a0a0bad64a50d6 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98ebd4148190b71b134d7545fe35 |
completed | April 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69ccd58bd21881908b860e3ee469af13 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93e90048190a2b0d7c5c195ba98 |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:01 p.m.