Triple
T2459930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bombay Stock Exchange |
E54508
|
entity |
| Predicate | numberOfConstituentsInFlagshipIndex |
P5741
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [Bombay Stock Exchange, numberOfConstituentsInFlagshipIndex, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfConstituentsInFlagshipIndex Context triple: [Bombay Stock Exchange, numberOfConstituentsInFlagshipIndex, 30]
-
A.
numberOfConstituents
chosen
Indicates the total count of individual components or members that make up a larger whole or group.
-
B.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
-
C.
hasNumberOfMemberInstitutions
Indicates the quantitative count of member institutions associated with a given entity.
-
D.
numberOfIndicators
Indicates the total count of indicators associated with or relevant to a given entity or context.
-
E.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
- 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_69ab49dee84c819096b50a0049c347ac |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd49c5aa081909ab4f726a458b77f |
completed | March 7, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69abd0b199488190aa381b36593ae1ac |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:44 p.m.