Triple
T3267448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BESIX |
E68559
|
entity |
| Predicate | hasDivision |
P35
|
FINISHED |
| Object | BESIX International |
E68559
|
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: BESIX International | Statement: [BESIX, hasDivision, BESIX International]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BESIX International Context triple: [BESIX, hasDivision, BESIX International]
-
A.
BESIX
chosen
BESIX is a major Belgian construction and engineering company known for delivering large-scale, high-profile projects worldwide.
-
B.
Hochtief
Hochtief is a major German-based global construction group known for large-scale infrastructure, engineering, and building projects worldwide.
-
C.
Hollandse Beton Groep
Hollandse Beton Groep was a major Dutch construction and civil engineering company known for its role in large-scale infrastructure and hydraulic engineering projects.
-
D.
Tractebel
Tractebel is an international engineering and consulting company specializing in energy, water, and infrastructure projects.
-
E.
Dalkia
Dalkia is a French energy services company specializing in energy efficiency, district heating and cooling, and sustainable energy solutions for buildings and industry.
- 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_69ad8590444081909e8107a8aeef3a23 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adafcf9c6c819092f9c618b778b46d |
completed | March 8, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3341597448190805ff43effb9070c |
completed | March 12, 2026, 9:45 p.m. |
Created at: March 8, 2026, 3:09 p.m.