Triple
T16081249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yanbu al-Sinaiyah |
E390114
|
entity |
| Predicate | hasMajorCompany |
P588
|
FINISHED |
| Object | SABIC |
E222488
|
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: SABIC | Statement: [Yanbu al-Sinaiyah, hasMajorCompany, SABIC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SABIC Context triple: [Yanbu al-Sinaiyah, hasMajorCompany, SABIC]
-
A.
SABIC
chosen
SABIC is a major Saudi-based global petrochemicals and plastics manufacturer known as one of the world’s largest chemical companies.
-
B.
BASF
BASF is a major German chemical company and one of the world's largest producers of chemicals and related products.
-
C.
Lanxess
Lanxess is a German specialty chemicals company known for producing high-performance plastics, rubber, and chemical intermediates for various industrial applications.
-
D.
Ineos
Ineos is a large multinational chemicals and energy company based in the United Kingdom, known for its extensive portfolio of petrochemical, oil, gas, and manufacturing operations worldwide.
-
E.
Invista
Invista is a global manufacturer of polymers and fibers, best known for producing materials used in textiles, carpets, and industrial applications.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1844a5c68819086a13c93a787b436 |
completed | April 17, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe48adec081909623355eabee472c |
completed | May 10, 2026, 1:51 a.m. |
Created at: April 10, 2026, 4:57 a.m.