Triple
T11259060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AltX |
E266513
|
entity |
| Predicate | targetIssuerSize |
P98769
|
FINISHED |
| Object | small-cap |
—
|
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: small-cap | Statement: [AltX, targetIssuerSize, small-cap]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetIssuerSize Context triple: [AltX, targetIssuerSize, small-cap]
-
A.
targetIssuerProfile
Indicates a relationship where a specific profile is designated as the primary or intended issuer in a given context.
-
B.
typicalIssuer
Indicates that one entity is the standard or commonly expected issuer (e.g., of a document, instrument, or credential) for another entity.
-
C.
headquartersOfIssuer
Indicates the location that serves as the main headquarters of the issuing entity.
-
D.
issuerOwnsBusinessesIn
Indicates that the issuer owns one or more business entities that are located or operate within the specified place.
-
E.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
- 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_69d6aac7953c8190b82caf9d7640fdf9 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e936cb048190b4d6fb2851ef8932 |
completed | April 9, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69d78793c00481908a3f764b610b77a4 |
completed | April 9, 2026, 11:03 a.m. |
| PDg | Predicate description generation | batch_69d796cf74308190a5b29d0dd82954a2 |
completed | April 9, 2026, 12:08 p.m. |
Created at: April 8, 2026, 9:31 p.m.