Triple
T16747654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International DOI Foundation |
E406996
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | ISTIC |
E406996
|
NE 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: ISTIC | Statement: [International DOI Foundation, hasMember, ISTIC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ISTIC Context triple: [International DOI Foundation, hasMember, ISTIC]
-
A.
ISTIC
chosen
ISTIC is a member organization of the International DOI Foundation, contributing to the global management and implementation of Digital Object Identifiers.
-
B.
STAT
STAT is a U.S.-based media company and news site focused on in-depth coverage of health, medicine, and life sciences.
-
C.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
-
D.
Stat.
Stat. is the standard legal citation abbreviation for the United States Statutes at Large, the official chronological compilation of all federal laws and resolutions enacted by the U.S. Congress.
-
E.
Statistics
Statistics is a Julia standard library module that provides basic statistical functions such as means, variances, and related summary measures for numerical data.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa2439848190a86a5bfc0702e2fe |
completed | April 18, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a52033748190ae207d72d437236b |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:21 a.m.